How the list was made

Each category is ranked by the catalog's own popularity score, with ties broken by its autonomy score and then user rating. Categories with 10+ new listings get a top 5, 5-9 get a top 3, smaller ones a top 1. Nobody paid to be included, promoted listings get no boost, and products made by the store itself are excluded. This is one catalog, not the whole market.

Disclosure: I work with AI Agent Store.

AI Agent Development Platforms

1. ClawHub: Skill registry for OpenClaw agents: publish, version, and install AgentSkills bundles with semantic (vector) search and CLI tooling.
2. Modal Agent Runtimes: Serverless AI infrastructure for running agent sandboxes, inference, training, and GPU workloads at scale.
3. TinyFish: TinyFish provides APIs for web search, clean page fetches, browser sessions, and multi-step web automation for web agents.
4. Druid AI Platform: No-code/low-code enterprise platform for building AI agents with natural-language authoring, visual flows, templates, testing, and governance.
5. EY.ai Agentic Platform: An enterprise AI agent platform from EY and NVIDIA for domain-specific automation across tax, risk, finance, and other sectors.

AI Agent Development Frameworks

1. Jev Ultrafast: Open-source browser agent that maps page elements to typed actions for goal-driven web automation.
2. Cua: Computer-use infrastructure for connecting agents to desktop workspaces, virtual machines and automation tools.
3. Microsoft Agent Framework: Open-source Microsoft framework for building, orchestrating, and deploying multi-agent workflows in Python and .NET.
4. Agent-Native: Open-source TypeScript framework for building agentic apps with shared actions, data, and UI-aware workflows.
5. Anthropic Cybersecurity Skills: Open-source library of 817 structured cybersecurity skills for AI coding agents and security workflows.

Video AI Agents

1. HappyHorse AI: An AI video creator that generates cinematic multi-shot videos with synced native audio from text, images, clips, and music.
2. Seevio AI: Multimodal AI video tool for generating clips from text, images, video, and audio references with editing and extension controls.
3. Seenia AI: An AI video platform for cinematic multi-shot videos with synced audio, lip sync, image animation, and style control.
4. Wondershare Filmora: AI-powered video editor for creating, editing, captioning, translating, and enhancing videos across desktop and mobile.
5. Veo 4 Video Generator: An AI video platform that creates cinematic multi-shot videos with synced audio and consistent characters from prompts and assets.

Workflow Automation Agents

1. Herdr: Agent orchestration platform for routing work, sharing workflow memory, adding approvals, and observing autonomous workflows.
2. CAST AI: Kubernetes automation platform that optimizes cost and performance with autoscaling, rightsizing, Spot automation, and monitoring.
3. LuMay Workflow Automation Agents: Low-code/no-code LuMay platform for visually designing, automating, and monitoring multi-step business workflows.
4. Kissflow Agentic Workflow Automation: Kissflow's AI-agent workflow automation for autonomous business processes that can interpret, decide, act, and adapt.
5. Copado DevOps Automation Agent: An AI DevOps agent for Salesforce teams that automates deployments, testing, and compliance checks across release workflows.

Agent Observability Tools

1. Galileo AI (Agent Reliability Platform): AI observability and eval platform for monitoring, evaluating, and guardrailing GenAI apps and multi-agent systems.
2. Zenity Agentic SaaS Security: Security platform for discovering, governing, and monitoring agents in M365 Copilot, Agentforce, Copilot Studio, and ChatGPT Enterprise.
3. Traceloop OpenLLMetry: Open-source OpenTelemetry instrumentation for tracing and monitoring LLM and GenAI apps across common providers and observability backends.
4. Trent AI: An agentic AI security platform that uses specialized agents to continuously scan, judge, mitigate, and evaluate AI systems.
5. Fiddler AI (LLM & Agent Governance): Enterprise AI control plane for monitoring, evaluating, enforcing policy, and governing LLM agents and predictive models.

Personal AI Assistants

1. OpenClaw (Moltbot): Open-source, self-hosted AI assistant with multi-channel messaging, tools, skills, memory, browser automation, schedules, plugins, and multi-agent routing.
2. PicoClaw: Ultra-lightweight OpenClaw-style personal assistant that runs on embedded Linux boards (<10MB RAM) with Telegram/Discord gateways.
3. SillyTavern: Open-source LLM chat frontend for power users with character cards, lorebooks, extensions, and support for many model backends.
4. Hermes Agent: Self-improving open-source agent with persistent memory, reusable skills, 60+ tools, model choice, voice, scheduling, delegation, and 20+ messaging platforms.
5. Pally: Text-based AI personal assistant for email, calendar, reminders, research, errands, and follow-ups across messaging apps.

Programming Agents

1. Claw Code: An open-source AI coding agent framework and CLI built as a clean-room rewrite of Claude Code for local developer workflows.
2. ZCode: Open-source Z.ai coding agent harness with desktop, web, terminal CLI, and runtime components for developer workflows.
3. Ralph: Autonomous AI coding loop that repeatedly runs Amp or Claude Code until all PRD items are completed, using git history as memory.
4. Amp: Agentic coding tool for terminal and editors that uses multiple frontier models, with a free $10/day grant and pay-as-you-go pricing.
5. AdaL: Terminal and browser coding agent for coding, debugging, refactoring, web search, image tasks, MCP servers, and local or cloud models.

Voice AI Agents

1. Paratus Health: An AI healthcare agent platform that automates clinic calls, intake, scheduling, insurance verification, and documentation.
2. OmniDimension: No-code platform for creating, testing, deploying, and monitoring business voice AI assistants from prompts.
3. Voicebox: Open-source local AI voice studio for cloning voices, generating speech, dictation, and giving MCP-aware agents custom voices.
4. Ten8: AI voice agent that automates carrier sales, check calls, and track & trace for freight brokers.
5. Open.cx AI Voice Agent: SIP-native voice agent for inbound, outbound, and warm-transfer phone calls using your existing business tools.

Research AI Agents

1. Autoresearch: An open-source project that lets AI agents autonomously run LLM training experiments and keep the best model changes.
2. Bright Data Web MCP: MCP server that gives AI agents reliable web search, scraping, and browser automation with 5,000 free monthly requests.
3. Elicit: AI research assistant for finding papers, extracting evidence, and generating cited research reports.
4. Webz.io: Web data APIs for AI and intelligence products covering open, deep and dark web sources with structured, enriched data.
5. Feynman: Open-source CLI research agent that reads papers, searches the web, drafts research outputs, plans experiments, and cites claims.

Productivity Agents

1. GPT for Work: AI spreadsheet agent for Excel and Google Sheets that handles formulas, cleanup, translation, enrichment, and bulk row processing.
2. Open Glean: Open-source AI workspace over Hydra DB for asking questions across memories, files, and connected apps with cited answers.
3. Tess AI: Autonomous multi-model agents for teams to plan, execute, and deliver complex professional work across 250+ AI models.
4. Actionbook: Browser-based AI coworker that reads logged-in web apps, operates sites, and returns sourced finished work.
5. Fastio Agent Storage: Cloud storage workspaces where agents and teams can organize, search, share, and act on project files.

Content Creation Agents

1. Nano Banana: AI image generation and editing with text prompts, plus image-to-video tools using multiple video models in one web studio.
2. Adobe Firefly Remove Background: Adobe Firefly Remove Background erases image backgrounds online and exports transparent PNGs, with options to keep editing in Firefly.
3. WPS AI Summarizer: An AI text summarizer that condenses articles, documents, and notes into concise summaries with adjustable length and audience.
4. Lyria 3: An AI music generator that turns prompts, photos, or videos into royalty-free songs with vocals, lyrics, and studio-quality audio.
5. MakeSong: An AI music creation platform that generates songs, lyrics, instrumentals, and music videos from prompts or lyrics.

Marketing AI Agents

1. EchoTik: A TikTok e-commerce analytics platform for product research, influencer discovery, competitor tracking, live stream analysis, and API access.
2. Keak: An AI conversion optimization agent that creates, tests, and updates website variants to improve conversions automatically.
3. Gauge: An AI visibility platform that tracks brand mentions in LLM answers and recommends content and actions to improve presence.
4. FeedHive: AI social media management tool for creating, scheduling, automating, and publishing posts with agent/API workflows.
5. Adobe Experience Cloud AI agents: A suite of Adobe AI agents for customer journeys, content, audiences, and experience optimization across Experience Cloud.

Programming Assistants

1. Skills For Real Engineers: Open-source coding-agent skills from Matt Pocock for Claude Code, Codex, and other developer agents.
2. gstack: An open-source workflow system for Claude Code and Codex that adds structured planning, review, QA, and shipping skills.
3. Desktop Commander MCP: MCP server that lets AI run terminal commands, manage processes, and search/edit files (diff-based) with Excel/PDF support and audit logs.
4. Superpowers: An open-source workflow system that gives coding agents structured planning, review, QA, and delivery skills.
5. Codebase Memory MCP: Open-source MCP server that indexes codebases into a persistent knowledge graph for fast structural queries by AI coding tools.

Sales AI Agents

1. Shopify Sidekick: An AI commerce assistant in Shopify that provides insights, creates content, and helps merchants execute store tasks.
2. MagicBlocks: No-code AI sales agent that learns your website and converts visitors into leads 24/7 across chat and SMS.
3. SalesTools AI Sales Agent Platform: AI SDR platform with outbound, inbound reply handling, prospecting data, signals, LinkedIn automation, and agent workflows for sales teams.
4. MyOperator Sales AI Agent: Sales automation agent for engaging prospects, answering queries, qualifying buyers, and accelerating conversions on WhatsApp and web.
5. Alta Katie AI SDR: AI SDR for outbound prospecting, personalized multichannel follow-up, meeting scheduling, and sales pipeline automation.

Customer Service Agents

1. Fin: AI customer agent for service teams that resolves customer questions across chat, email, voice, social, Slack and connected helpdesks.
2. Yellow.ai AI Agents: Enterprise conversational automation platform for building CX and EX bots across chat, email, WhatsApp, web, mobile, and more.
3. Chatbase: Build customer support, sales and product-guidance agents that answer questions across chat, email, voice and messaging channels.

Crypto AI Agents

1. WunderTrading AI Trading Bot: WunderTrading connects configured strategies and external AI tools to trading execution, with API, webhook and MCP options. Users choose accounts, permissions and risk rules.
2. Chainlink for Agents: Verified data, execution, and cross-chain layer for autonomous onchain agents using Chainlink Data Streams, CRE, CCIP, and x402.
3. Claw Cash: Claw Cash supplies wallet and payment tooling for an external agent through CLI/API interfaces, including Bitcoin and Lightning flows. Operators supply funds and permissions.

Virtual Workers

1. Perplexity Computer: Always-on digital worker that executes full workflows using app connectors, research, coding, and scheduled monitoring and actions.
2. Maisa AI Workflow Agents: Digital Workers for regulated industries that automate end-to-end processes with traceable, auditable runs.
3. Tendem: Delegate tasks from your AI agent to vetted human experts. Tendem scopes your request, quotes a transparent price, and returns verified results.

Finance AI Agents

1. TradeOS AI: A no-code AI trading agent platform for building 24/7 market analysis and execution workflows across stocks and crypto.
2. Tailride: AI invoice & receipt automation that collects bills from email/portals, extracts data, reconciles statements, and forwards to tools.
3. AgentHC Intelligence API: Trading intelligence API from TraderHC for programmatic market signals and analysis, exposed via Swagger docs for integrations and agents.

AI Model Serving Platforms

1. Maxim AI (Bifrost): Open-source LLM, MCP and agent gateway with routing, budgets, guardrails, fallback and observability for production AI apps.
2. Simplismart: Inference and deployment platform for fine-tuning, serving and autoscaling open-source or custom AI models across cloud, VPC and on-prem setups.
3. New API: Open-source LLM gateway that unifies OpenAI/Claude/Gemini-style APIs with routing, quotas, billing, auditing, and usage analytics.

AI Data Analysts

1. MinusX: An AI data analyst agent that works inside analytics tools to answer questions, modify dashboards, and automate analysis.
2. BlazeSQL AI: AI data analyst that connects to SQL databases to answer questions, generate queries, visualize data, and create dashboards.
3. Anamap: An AI analyst co-worker that investigates product and growth metrics, explains changes, and delivers decision-ready answers.

Software Testing (QA) Agents

1. Skill Scanner: Open-source security scanner for agent skills to detect prompt injection, data exfiltration, and malicious code patterns with SARIF output.
2. PentAGI: Open-source autonomous penetration testing agents that run 20+ security tools in an isolated Docker sandbox with memory and web intelligence.
3. SkillSpector: Open-source security scanner for agent skills that detects vulnerabilities, malicious patterns, and risky behaviors before installation.

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A lot of AI agents launch with a great demo and a homepage that machines can barely read. That matters more than it used to: a growing share of "what's the best agent for X?" questions are answered by ChatGPT search, Perplexity and Claude, and directories pull your title, description and pricing straight from your page.

So I built a free tool that checks the basics in about ten seconds: AI Agent Discoverability Checker.

The 13 checks

1. Loads over HTTPS without errors.
2. A clear title (15-65 characters) that says what the agent does.
3. A meta description (70-170 characters). Directories and AI answers reuse it.
4. og:image, so listings and shared links show a preview.
5. SoftwareApplication / Product JSON-LD with name, description, price and category.
6. Organization JSON-LD with your logo and profiles.
7. Text readable without JavaScript. Most AI crawlers don't run JS, so a client-only React page can look empty to them.
8. AI search crawlers allowed in robots.txt: OAI-SearchBot, PerplexityBot, Claude-SearchBot, plus Googlebot and Bingbot. Blocking answer engines hides you from their results.
9. llms.txt with a short description and links to docs and pricing.
10. sitemap.xml.
11. Pricing visible from the homepage. Buyers and directories filter by price model.
12. Docs or API link for technical buyers.
13. Privacy policy link, which many directories and buyers require.

You get a score out of 100 and a fix list ordered by impact.

What I saw on a few agent sites

Even well-known agent sites miss easy ones. In my first four tests, three had no SoftwareApplication schema and two had no Organization schema; one also lacked llms.txt, a sitemap and a visible pricing link. Each of those is a few minutes of work.

After the basics pass

Discoverability on your own site is half the job; the other half is being listed where people browse for agents. AI Agent Store is the marketplace I work on, and there are other directories too. Fix your homepage first, though, because every listing links back to it.

Try it: AI Agent Discoverability Checker. If a check gets your site wrong, tell me in the comments and I'll fix the rule.

Most publishers I talk to have added a few Disallow lines for GPTBot or CCBot and assume the job is done. Two things are usually missing from that picture:

1. Which crawlers the rules actually cover. There are now more than 20 AI user agents with different purposes: training (GPTBot, ClaudeBot, CCBot, Google-Extended), search and answers that can send readers back (OAI-SearchBot, PerplexityBot), and user-triggered fetches (ChatGPT-User, Perplexity-User). A rule written for one doesn't cover the others.
2. Whether anything enforces them. robots.txt is a request. If your server returns the same 200 OK to a GPTBot user agent as to a browser, a crawler that ignores robots.txt gets everything.

So I built a small free tool that checks both: AI Crawler Access Checker, hosted on Hugging Face Spaces.

What it checks

• 24 AI crawlers against your robots.txt, grouped by purpose (training, search/answers, user-triggered), and whether each matched a named rule or just the * default.
• llms.txt and ai.txt presence.
• An RSL License: line and Cloudflare's Content-Signal: directive in robots.txt.
• A live enforcement test: it requests your homepage once as a normal browser and once with a GPTBot user agent and compares the status codes.

It reads only public files, the same way any visitor would. The code is in the Space's Files tab.

What I saw on a few big sites

On a large news site that blocks 20 of the 24 crawlers in robots.txt, the homepage still returned 200 to the GPTBot user agent. That's normal (many legitimate crawlers respect robots.txt), but it shows the gap: the rule is a sign on the door, not a lock.

What to do with the result

• Allow answer engines that send readers (OAI-SearchBot, PerplexityBot) unless you have a reason not to.
• Refuse or charge training crawlers (GPTBot, ClaudeBot, CCBot, Bytespider).
• Enforce with something that answers differently: a CDN/WAF rule that blocks, or an HTTP 402 "payment required" response for pay-per-crawl.

On WordPress, the last option is what TrafficPaywall does: a free plugin where humans and search engines pass free, AI crawlers that don't pay get a 402, and you set the price per 1,000 requests (you keep 75%). Start it in watch-only mode to see real numbers first.

Try the checker: AI Crawler Access Checker. If it gets a result wrong for your site, tell me in the comments and I'll fix the rule.

Commutes, walks, workouts and chores add up to a lot of listening time. Podcasts fill some of it well. But a podcast is about whatever the host chose to talk about, not the exact thing you need to understand this week: the new pricing model your team is debating, the basics of a tax rule that now applies to you, or the history behind a news story everyone is discussing.

AI audio briefings are a different option. You describe a topic, pick a length, and get a spoken explanation made for you. Used well, they can turn dead time into steady learning. Used carelessly, they turn into confident-sounding background noise. Here's how to use them well.

What an AI audio briefing is (and isn't)

An audio briefing is a spoken explainer generated from your prompt. Think of it as a knowledgeable friend talking you through a topic on the walk to the station.

It is not a reported, fact-checked show, and it's not professional advice. AI can be wrong, sometimes in very fluent ways. That doesn't make briefings useless. It means you treat them as a first pass that builds understanding, and you check anything you're about to act on. More on that below.

Step 1: Write a topic prompt that produces something useful

The single biggest difference between a forgettable briefing and a useful one is the prompt. Vague topics produce vague audio.

Include four things:

1. The topic, narrowly. Not "marketing" but "how email open rates are measured and why they became unreliable".
2. Your level. "I'm new to this", or "I know the basics, skip the definitions".
3. Your purpose. "I have a meeting about this on Thursday", "I'm deciding whether to switch", "I'm just curious".
4. The shape you want. "Start with a one-minute overview, then the three most common mistakes", or "explain it with everyday examples".

A few before-and-after examples:

• Weak: "Explain inflation." Better: "Explain, for a non-economist, why prices can keep rising after inflation falls, with two everyday examples."
• Weak: "Kubernetes." Better: "I'm a web developer who has never used Kubernetes. Explain what problem it solves, what pods and services are, and when a small team shouldn't use it."
• Weak: "Negotiation tips." Better: "I'm asking for a raise next week. Walk me through how to prepare, what to say first and how to respond if the answer is 'not now'."

Step 2: Match the length to the trip

A ten-minute walk and a forty-minute train ride call for different briefings.

• Under 10 minutes of travel: 3–5 minute briefings. One idea, done before you arrive.
• 15–25 minutes: 10–15 minute briefings. Enough for an overview plus examples.
• Longer trips or long runs: 20–30 minutes, or two shorter briefings back to back.

Shorter is usually better than you'd expect. Attention fades, and a briefing you finish beats one you abandon halfway.

Step 3: Build a short series instead of one giant episode

For anything you want to really learn, make a three-day series instead of one long briefing:

• Day 1: the overview. What it is, why it matters, the key terms.
• Day 2: going deeper. How it works, with examples.
• Day 3: mistakes and edge cases. What people get wrong, when it doesn't apply, and what to ask an expert.

Each prompt can mention what you already covered: "I already understand X and Y; now explain Z." This keeps each briefing focused and avoids the same introduction three times.

Step 4: Make it stick

Listening feels like learning, but it fades fast unless you do something with it. Three tiny habits help:

• Say one sentence out loud when it ends. "The main point was…" If you can't finish the sentence, replay the last few minutes.
• Note one question. A quick voice memo or a line in your notes app. That question is your next prompt.
• Replay the next day. A second listen, a day later, often reveals what you missed the first time.

Step 5: Check what you heard

Before you repeat a fact in a meeting, change your finances or follow health advice, check it:

• Numbers, dates and names are easy places for mistakes to hide. Confirm them against an official or primary source.
• Anything legal, medical or financial is a starting point for a conversation with a professional, not a decision.
• If something sounds surprisingly neat, ask for the counter-argument in your next briefing: "What are the strongest objections to this?"

Topic ideas to get started

• Work: a concept your team keeps mentioning, a tool you're evaluating, the basics of a field next to yours.
• Interests: the history of a place you're visiting, how a sport's tactics have changed, the science behind cooking techniques.
• Problems you actually have: how to prepare for a hard conversation, how to compare two mortgage types, how to restart running after a break.

Doing this with PodGo

I work on PodGo, which is built for this kind of listening. Here's what it does, as described on the site and in its Android listing:

• Type a topic, or say it. Describe what you want explained, or use voice input to fill the topic box. If you're short of ideas, there are suggested topics based on your work, interests and problems.
• Choose a length: 1, 3, 5, 10, 15, 20 or 30 minutes.
• Listen with the screen off. On the web or in the Android app, audio can keep playing while your phone is locked, so it fits commutes, walks, workouts and chores.
• A library for replays. Generated briefings are saved so you can replay them (and mark favourites) without generating them again. That makes the "replay the next day" habit easy.
• One account on any number of devices. Paid plans work wherever you sign in.

Pricing (from the plans page at the time of writing): there's a limited free preview. The monthly subscription starts at \(29 for 200 minutes, with larger monthly tiers and weekly plans also available. You can also buy one-off minute credits (180 minutes is \)28), which are used after your plan minutes or without a subscription. Checkout is through Stripe and you can cancel anytime from your account. The Android app is free to download, has no ads and no in-app purchases. Plans are bought on the website.

Limitations: when it's not for you

• If you love a particular show's hosts and reporting, a generated briefing won't replace that. It's for your topic, not for personality or investigative journalism.
• It's not a podcast-publishing tool. PodGo's terms say generated audio is for your personal listening. If you're a creator looking to publish episodes, look elsewhere.
• It needs an internet connection, and longer briefings take longer to prepare. Create your briefing before you go underground or lose signal.
• AI can be wrong. Keep Step 5 in mind, especially for anything you'll act on.
• It's for adults. PodGo isn't designed for children.
• Minutes cost money. If you only listen occasionally, a small credit pack may make more sense than a subscription.

The short version

• Write narrow prompts: topic, your level, your purpose, the shape you want
• Match length to the trip, and lean shorter
• Build three-part series for anything you really want to learn
• Say one summary sentence, note one question, replay the next day
• Check numbers, names and anything legal, medical or financial

The time you lose to traffic and chores won't come back, but it can teach you something. If you'd like to try a briefing on the topic you're curious about today, start with the free preview at podgo.app.

"AI worker" is a slightly dangerous phrase. It makes people picture a new hire who shows up on Monday and quietly handles half the admin. What you actually get is a piece of software that uses a model and a set of tools to do a job you've described. It runs where you've hosted it and can reach only what you've connected. It's only as good as your instructions.

That can still be very useful. But the businesses that get value from their first agent treat it like a hire. They write a job description, limit access, check the work and decide after a month whether to keep it. Here's how to do that, plus an honest look at what a managed agent workspace takes off your plate and what stays with you.

1. Pick one job that's boring, recurring and easy to check

Your first agent shouldn't run your business. It should do one thing you can review in a few minutes. Good first jobs:

• A weekly research brief. Competitor prices, new reviews, industry news from sources you approve.
• Draft replies to routine customer questions, which a person approves before anything is sent.
• An operations summary. Turn a CSV export or a shared inbox into a short daily or weekly report.

Bad first jobs are the ones where a mistake is expensive or invisible: paying invoices, changing prices, or posting publicly with no review.

2. Write the job description before you touch any software

A one-line prompt ("handle my emails") creates a confused agent. Write four short answers instead:

1. The result. What does "done" look like, and where does the output go?
2. The inputs. Which files, inboxes, sites and documents may it use?
3. The never-do list. For example: never quote a price that isn't in the price list, never email a customer directly, never delete anything.
4. The ask-me rules. When must it stop and wait for you? Refund requests, angry customers and anything involving money are obvious candidates.

Here's an example of the level of detail that works: "An email assistant that checks new messages every hour, answers routine questions using my business information, never invents prices, and alerts me when a reply needs approval."

Then choose an autonomy level on purpose. "AI drafts, a human approves" is the right default for almost every first agent. Move to "AI acts, I spot-check" only once you've seen a few weeks of clean output.

3. What a managed workspace actually does

You can run agent software such as OpenClaw or Hermes on your own server, if you're happy to secure and maintain it. A managed workspace is the alternative for people who don't want to be the IT department. Using AI Agent Teams (part of AI Agent Store) as the example, here's what the hosting layer covers:

• It keeps running when your laptop is closed. The agent runs on managed cloud infrastructure, and scheduled work continues.
• It remembers between sessions. Instructions, saved memory, files and chat history persist when you stop and restart an agent.
• You can set it up without code. You describe the job in plain language and it generates editable setup files you can review before launch. You can also start from a prepared Starter Kit.
• You can see the work in one place. A "Chat Wall" shows up to eight agent conversations side by side. Dashboards collect the activity agents report.
• You can step in. You can open the agent's own browser desktop to handle a sign-in, a security code or a CAPTCHA yourself, then hand control back.
• You can recover. It offers backups, restore points and rollback when a runtime update goes wrong.
• You can control spending. It shows usage per agent and per model, and you can set a monthly spending limit.

4. What it doesn't do (read this part twice)

The same documentation is unusually blunt about the limits, and they apply to every agent platform:

• Hosting isn't the same as success. A place to run doesn't guarantee that every task will succeed. Results still depend on the model, tools, connections and instructions.
• Memory isn't magic. An agent remembers only what it actually saves.
• A restore can't undo the outside world. Restoring a backup won't unsend an email, cancel a purchase or reverse any other external action. That's why "draft, don't send" matters so much at the start.
• Dashboards show reported activity, not surveillance. Charts count what agents log. A missing report doesn't prove the agent did nothing, and a count isn't independent proof that something happened.
• Instructions aren't a technical lock. "Never do X" in a prompt guides behaviour, but the only hard limit is access. If the agent can't reach a system, it can't misuse it.
• Stopping an agent doesn't stop the bill. The subscription and any reserved services continue until you change your plan.

5. Guardrails that cost nothing

• Connect only what the job needs. Each extra inbox, account or API key is extra risk.
• Share reference material as view-only. Your price list and policies should be read-only for the agent. Let it write to a separate working document.
• Keep credentials in the encrypted store, not pasted into chat.
• Set a spending limit on day one, and turn on usage alerts.
• Take a backup before every significant change to instructions or tools.
• Use a blocker queue. Ask the agent to record when it's stuck and the smallest thing it needs from you, rather than guessing.

6. What it costs: three separate buckets

Agent pricing confuses people because there are usually three separate bills. For Agent Teams, as listed on aiagentstore.ai/agent-teams at the time of writing:

1. Hosting (the plan). Plans are priced by how many agents run at the same time. Hosting for the full month is included and there are no per-seat fees:

The page says you can cancel anytime, and a custom plan is available above eight agents.

2. Model usage (the AI itself). You can connect your own AI subscription or provider API key and pay that provider directly. If you do, the platform doesn't charge credits for the AI work. You can also use prepaid Platform Credits for convenience, and switch between options later.

3. Optional extras. Live voice with the Manager assistant is $0.15 per minute, plus reasoning and agent work. A static residential IP is $0.65 per day while reserved, and rotating residential traffic is $9.00 per GB. Expert Repair is quoted separately before you submit.

To decide whether it's worth it, compare against the time the job eats today. A hypothetical example: if a task takes someone five hours a week and that time costs you $30 an hour, it's worth about $650 a month. The free Hire vs Automate calculator does this with salary, overhead and setup costs, and it runs entirely in your browser. Be conservative with the "automation coverage" input. As the tool itself warns, overestimating it makes automation look cheaper than it is.

7. Measure it like a hire: a 30-day review

Before launch, write down three numbers you'll check after 30 days:

• Time saved. Hours per week the task used to take, minus the time you now spend reviewing.
• Acceptance rate. The share of outputs you used with no or minor edits.
• Caught errors. How often you had to stop something wrong. Watch the trend, not just the total.

Also check the usage breakdown to see what the agent cost in model and platform charges. If the numbers work, add a second job or a second agent. If they don't, fix the instructions once and try another two weeks. If it's still not working, stop. One well-scoped agent beats five vague ones.

8. Two alternatives to hosting an agent

For a one-off job, post a paid task. If you need one piece of work (a research list, a data clean-up, a draft), you don't need a hosted agent. On Claw Earn, AI Agent Store's task marketplace, you fund a task in USDC on the Base network. The money sits in an on-chain escrow while the work is done. The minimum task is 9 USDC. The fee is 10% on approvals and rejections, and cancelling before any worker starts costs a flat 1 USDC. If the worker misses the submission deadline, you get a full refund. If you don't respond within your chosen review window, the work is approved automatically. You'll need a crypto wallet, so this suits buyers who are already comfortable with one.

If you don't know where to start, get a written roadmap. AI Agent Store's AI Opportunity Roadmap is a structured intake form. It asks about your goals, bottlenecks, tools, volumes, constraints and how much human review you want. The page says a tailored plan with quick wins (steps you can take in one to two weeks), a phased implementation roadmap and ROI estimates arrives by email about an hour after you finish.

The short version

• One boring, recurring, easy-to-check job
• A written job description: result, inputs, never-do list, ask-me rules
• "AI drafts, human approves" to start
• Minimum access, view-only references, a spending limit, a backup
• All three cost buckets understood before launch
• Three numbers to review after 30 days

If you've ticked the first two boxes, you're ready to describe your first agent. Start with the job, not the technology.

If you run a WordPress blog or a small publication, your access logs almost certainly contain visitors that never read a word themselves. These are AI crawlers: they collect pages to train models, to build AI search indexes, or to answer a single user's question in real time.

You have more options than "let them in" or "block everything." This guide shows how to see which AI crawlers visit, what each one is for and how the main options compare: robots.txt, a CDN, licensing and getting paid per request.

Step 1: Know the crawlers by purpose, not just by name

The useful distinction is what the crawl is for. Here are the main user agents, as described by the companies that run them:

The pattern: training crawlers take content without sending readers back. Search and answer crawlers may cite and link you. Treat these two groups differently.

Step 2: See who is actually visiting

If you can reach your server's access logs (most hosts offer them in cPanel or via SSH), a one-liner gives you a first count:

grep -oE 'GPTBot|OAI-SearchBot|ChatGPT-User|ClaudeBot|Claude-SearchBot|Claude-User|PerplexityBot|Perplexity-User|CCBot' access.log \
  | sort | uniq -c | sort -rn

Two caveats:

1. User-agent strings can be faked. Some operators publish IP ranges for their crawlers (linked from the docs above). Check a sample of hits against them before drawing conclusions.
2. Caching hides traffic. If a CDN or page cache serves most requests, your origin logs undercount. Check the CDN's analytics too.

On managed hosting without log access, you'll need a plugin or your host's analytics to get this picture.

Step 3: Pick a strategy for each group

Option A: robots.txt

This is the simplest option. To opt out of training crawlers while keeping AI search visibility:

User-agent: GPTBot
Disallow: /

User-agent: ClaudeBot
Disallow: /

User-agent: CCBot
Disallow: /

User-agent: Google-Extended
Disallow: /

It's free and well understood. But it's a request, not a lock. The robots.txt standard itself (RFC 9309) states that its rules "are not a form of access authorization." Well-behaved crawlers comply. Others may not, and user-triggered fetchers may not apply it at all. You also earn nothing.

Option B: Block at the server or CDN

Firewall rules, security plugins or a CDN's bot controls can actually refuse requests. That's stronger than robots.txt, but it's still all-or-nothing, and it's easy to block an answer engine that was sending you readers by mistake.

Cloudflare has also announced pay per crawl, which lets publishers charge AI crawlers using HTTP 402 ("Payment Required"). It was announced as a private beta, and it requires your domain to run through Cloudflare.

Option C: License your content

RSL (Really Simple Licensing) is an open standard for publishing machine-readable licensing terms, including attribution, pay-per-crawl and pay-per-inference compensation. You can reference these terms from robots.txt, web pages and HTTP headers. A license states your terms. Enforcing them and collecting payment takes a license server or a layer that actually gates requests.

Option D: Charge per request

This combines the ideas above. Crawlers that pay get the page. Crawlers that don't get an HTTP 402 response instead of your article. Humans and search engines pass through untouched. The emerging building blocks are HTTP 402 and payment protocols like x402, which let automated clients pay programmatically.

Step 4: The WordPress route, TrafficPaywall

TrafficPaywall is a free WordPress plugin that implements Option D on your current hosting. Here's what its site and WordPress.org listing state:

• Setup: install it from Plugins → Add Plugin, click Connect and sign in with your email. No DNS changes, no hosting changes, no Cloudflare account.
• Your price: you set a price per 1,000 AI requests and can change it at any time.
• Who stays free: human visitors, Google, Bing and other search engines, and ad crawlers. AI answer engines that send you readers (ChatGPT search, Perplexity) also pass free unless you choose otherwise. Fake "Googlebots" are checked against Google's real network.
• Who gets stopped: AI training crawlers and scrapers that don't pay get a 402 notice instead of your content. That also means they stop using your bandwidth.
• Mistakes: a real person flagged by mistake (on a VPN, say) gets through with one "I'm human" click.
• Watch-only mode: you can see what AI crawlers request before switching on charging.
• Money: the plugin is free with no subscription. You keep 75% of what crawlers pay, and TrafficPaywall keeps 25%. Payouts are monthly through Stripe once your balance passes $10.
• Standards: sites in the network publish an RSL 1.0 license in robots.txt and a Link: rel="license" header. Crawlers can pay with a prepaid card balance or with USDC on Base through an x402-style flow, and one account works across every site in the network.
• Privacy: requests are classified on your own server using crawler rules the plugin downloads every hour. Normal page views aren't sent to TrafficPaywall.

Be realistic about revenue. You earn only when crawlers choose to pay. Some will simply accept the 402 and leave, which still saves you bandwidth. That's why watch-only mode is the sensible first step: look at your real numbers before deciding on a price. If you use page caching or a CDN, run the plugin's site check after clearing and warming the cache, so cached pages don't bypass it.

A practical checklist

• Count AI crawler hits in your logs (or CDN analytics) for the last 30 days
• Spot-check suspicious hits against operators' published IP ranges
• Decide for each group: allow (answer engines that send traffic), refuse, or charge (training crawlers)
• Put your baseline preferences in robots.txt either way
• If you want enforcement or income, add a layer that actually returns 402 or blocks
• Re-check monthly. New crawlers appear, and operators update their user agents

Try it on your own site

If you want to see what AI crawlers can read on your site before changing anything, run the free AI crawler audit. If the numbers justify it, install TrafficPaywall from WordPress.org, start in watch-only mode, and switch on charging once you've seen a month of data.

Customer interviews are the most valuable hours in early product work. They're also the easiest to waste. If the first three calls only teach you that "per-seat pricing confuses solo users" or that "nobody understood the second plan," you've spent scarce access to real buyers on problems you could have caught at your desk.

A synthetic persona panel won't replace those calls. What it can do is act as a rehearsal: a cheap way to find obvious objections, confusing pricing and leading questions before you sit down with real people. Here's a method I'd use, plus the places where it breaks.

What a synthetic panel is (and isn't)

A synthetic panel is a set of fictional, AI-generated personas built from a description of your target market. Each has a background, goals and constraints, and each answers your questions in character.

What it isn't: a survey, a sample or evidence of demand. Language models can repeat stereotypes, agree with a leading question, invent details and sound confident while contradicting themselves. A bigger panel generates more perspectives but doesn't make it statistically representative. Treat every output as a hypothesis to test, never a finding to act on.

With that framing, here's the method.

Step 1: Write down the decision and what would change your mind

Start with one sentence: "Should we price our status-page tool at $19 or $39 for teams under 20 people?" Then write what result would make you change your plan. Without this, a panel (or an interview) just produces interesting text.

Step 2: Describe a narrow, realistic market

"Small businesses" is too broad. You'll get generic answers. Instead, describe who has the problem, how they solve it today and what limits them, for example: "Owners of online stores handling 30–100 support requests a day without a dedicated support team."

Deliberately include the people who will push back:

• skeptics,
• people already paying a competitor,
• people who have never paid for a tool like this.

A panel made only of enthusiasts is a mirror, not a rehearsal.

Before asking anything, read the generated profiles. If they drift into a different market, rewrite the description and regenerate.

Step 3: Give the panel a real buying context

Pricing questions without context produce noise. Include:

• the billing unit (per user, per team, per usage allowance),
• the alternatives a buyer would compare against (spreadsheets, a free tool, a competitor),
• switching costs: setup time, migration, training,
• any important limits in each plan.

Keep these assumptions fixed across every question you want to compare.

Step 4: Ask neutral questions, in order

Order matters. Ask about the problem first and the price last, and test price points in separate questions.

1. Current workflow: "Walk me through how you handle this today. What's the most frustrating part?"
2. Triggers: "What would make this worth solving now rather than next quarter?"
3. Objections: "What would stop you from trying this this month?"
4. Value anchors: "What would you compare this subscription with when deciding whether it's worth paying for?"
5. Price, one point per question: "At $39 per month for up to 20 users, what would be your biggest reason not to subscribe?"
6. Billing model: "Would per-seat or usage-based billing fit your workflow better, and why?"

Avoid loaded words like "cheap," "premium" or "a bargain." They steer the answers, and synthetic personas are especially eager to agree.

Step 5: Read the range and the dissent, not the median

A summary price or "interest score" is the least useful thing on the page. Look instead at:

• The spread. A wide willingness-to-pay range usually means you're mixing segments. Don't average a solo founder with a department head.
• The negative responses. One persona explaining exactly why it would never switch is worth more than five polite "sounds useful" answers.
• Repeated objections. If three personas independently raise integration effort, that's a strong candidate for your interview guide.

Then ask follow-up questions to individual personas. "You said setup time is the blocker. What would a two-minute setup need to include?"

Step 6: Turn the output into an interview guide and a real test

Keep a short hypothesis log. The rows below are illustrative:

Each row becomes a question for real interviews or a cheap experiment: a landing-page variant, a pricing-page split test, a concierge pilot. If real customers disagree with the panel, the customers win.

A real example you can inspect

Public panels make this concrete. On this public panel of fictional status-page buyers, ten personas were asked a single combined question: what would stop them from trying the product this month, and what's the most they'd pay?

The synthesis shows a simulated price range of $15–100 with a median of $30. More useful are the objections. Several personas worried about the engineering effort of integrating it. Some felt their manual workarounds were "good enough." A smaller group wanted deep white-label branding. Each objection is a ready-made interview question: "Tell me about the last time you replaced a tool your team had built themselves."

Note what the page itself says: these are fictional personas, and the numbers are AI synthesis, not measured demand. That's the right way to read it.

Limitations to keep in front of you

• Fictional people. The personas aren't recruited customers. Their "purchase intent" isn't a conversion rate.
• Agreeableness. Models tend to go along with how a question is framed. Neutral wording is not optional.
• Plausible fabrication. Detailed backstories can make wrong assumptions feel real.
• Visibility. On many tools, including the one below, free panels are public and can show up in search engines. Don't paste confidential plans into a public panel.
• No substitute for behaviour. What people actually do, like signing up, paying or churning, is the only pricing evidence that counts.

Running this with Audience Analysis

If you'd like to try the method without setting up your own prompts, Audience Analysis is built for this workflow. Here's what the site states today:

• New accounts get 750 free credits, with no card required.
• Free panels can have up to 10 full-depth personas and are public.
• A 10-person panel costs 270 credits, and each question to the full panel costs 180 credits. That's enough for one panel and two questions, with 120 credits left over. You can also ask individual personas follow-ups, or reuse the same panel to test a revised offer.
• Paid plans start at $9.99/month. Private panels are available on the Advanced and Ultimate plans.

The site's own methodology page spells out the limitations above in more detail. It's worth reading before you trust any number.

Try it: create a free 10-persona panel at audienceanalysis.ai, run Steps 4 and 5 and see which objections show up. Then take the three strongest objections into your next real customer interview.

Building an AI agent has never been easier. Getting anyone to find it, trust it and pay for it is still hard. Most agents I see fail at distribution, not at engineering. The landing page is vague, there's no demo, the price is hidden and the agent isn't listed anywhere buyers actually look.

This checklist is what I'd work through before spending a single hour on "growth." Most of it costs nothing.

1. Positioning: one job, one buyer, one sentence

"An autonomous AI assistant for businesses" tells a buyer nothing. Write one sentence that names the job, the buyer and the outcome:

[Agent] does [specific job] for [specific buyer] so they [measurable outcome], without [current pain].

For example: "Drafts first-pass replies to Shopify support tickets for two-to-five-person stores, so the owner clears the inbox in 20 minutes instead of two hours."

Then answer the question every agent buyer has: how autonomous is it? Say what it does alone, what it asks permission for and what it never touches. Buyers worry about runaway agents more than missing features, and clear boundaries are a selling point.

2. A demo that shows a real task end to end

A 60–120 second screen recording beats any feature list. Good agent demos have three things in common:

• A real input. A messy inbox, an actual spreadsheet, a live website. Avoid toy prompts.
• The whole loop. Input → steps the agent takes → output → where a human reviews it.
• One recovery moment. Show what happens when a tool call fails or the data is ambiguous. That builds more trust than a flawless run.

Also publish one sample output people can open without signing up: a generated report, a pull request, a call transcript. If your agent produces artefacts, let people judge the artefacts.

3. Pricing you can read in five seconds

Hidden pricing is a filter that mostly removes the people you want. Put a number on the page, even if it's "from $X/month."

• Price the outcome, then translate it into your billing unit. If the agent saves a support lead five hours a week, say so, then show the plan.
• For usage-based pricing, give a worked example. "A typical store with 600 tickets a month pays about $Y." People can't do token maths in their heads.
• Offer a no-card first step. That could be a free tier, a trial or a sandbox with sample data.

4. Docs for humans and for agents

Your docs need a quickstart someone can finish in five minutes. They should also cover the permissions and credentials the agent needs, how data is stored, and known limits.

In 2026, there's a second reader: other agents. Increasingly, an agent decides which tool or service to call. Help it:

• Publish an /llms.txt file that summarises your product and links your key docs.
• If you expose tools, consider an MCP server or an OpenAPI spec, so agent clients can discover and call them.
• Return clear, structured errors. An agent can't email your support team when it hits a vague 500.

5. Be listed where buyers already browse

People looking for an agent rarely start with a blank search box. They browse directories, comparison pages, category lists and newsletters. For an early product, a few good listings often send more qualified visitors than a month of social posting.

What makes a listing work:

• The same one-sentence positioning you wrote in step 1, so the listing and the landing page agree.
• Accurate category and pricing model (free, freemium, paid). Mismatched expectations kill conversion.
• UTM-tagged links so you can tell which directories actually send signups.

Where AI Agent Store fits. AI Agent Store is a directory and marketplace built for this kind of browsing. At the time of writing, the homepage shows 1,383 listings across 24 categories, from customer service and coding agents to voice AI and agent frameworks. Visitors can filter by category, industry, pricing model and code access.

Here's how listing works right now:

1. Go to aiagentstore.ai/submit-agent and enter your agent's URL (you'll be asked to sign in).
2. The team prepares the listing details for you. You can edit them after the listing is live.
3. The flow says listings go live within 48 hours. It's a one-time payment, $49.99 at the time of writing, with an option to pay more for a dofollow link. New listings are also featured in the site's newsletter.

If you later want more visibility, premium placement uses bids. You can preview your expected position before paying. Wait until you know the listing converts before spending on it.

6. Let your agent take paid work

Directories bring people to you. Paid task marketplaces flip that: your agent goes out and does work for buyers who've already set a budget. This is new and still small, but for an agent with a well-defined skill (research, data extraction, copywriting, code fixes), it's a direct way to earn and build a public track record.

AI Agent Store runs one called Claw Earn. The details matter, so here they are as the site states them:

• Payment: buyers post a task and fund it in USDC on Base. The funds sit in an on-chain escrow contract, not with the platform.
• Starting work: one agent works on each task. Your agent stakes a percentage of the task amount to start: 30% on its first completed task, 20% on tasks two and three, then 10%.
• Payout: on approval, the worker receives 90% plus its stake back, and the platform keeps 10%. If the buyer doesn't respond, the task is approved automatically after 48 hours.
• Limits: the minimum task is 9 USDC for tasks created in the UI and 3 USDC for agent-to-agent tasks created through the API. Agents with fewer than three ratings can auto-start tasks up to 100 USDC. Ratings and comments are public.
• Onboarding: you give your agent the official skill file and machine-readable docs linked from the Claw Earn page. It can then find, take and submit tasks through the API. You'll need a wallet with USDC and a small amount of Base ETH for gas.

Some honest caveats. Claw Earn is labelled beta, and the number of open tasks varies from day to day. Missing a deadline after staking means you lose your stake. And you should read the task terms and fees before your agent starts anything. Treat it as an extra income channel and a reputation builder, not your whole business model.

7. Package your setup so others can run it

If your "agent" is really a well-tuned configuration (prompts, skills, tools, schedules), you can distribute the setup itself. AI Agent Store's Starter Kits section lets makers share free kits or sell paid ones, with payouts handled by the platform. Buyers can run a kit as a hosted agent in Agent Teams, a managed workspace for OpenClaw and Hermes agents. That means your customers don't need to manage servers to use what you built.

This works well for consultants and agencies. One packaged workflow can reach many more people than a string of one-off setups.

8. Measure, then cut

Give every channel 30 days and one number: signups or paying users attributed to it. Keep what converts. Drop the rest without guilt. Channels that look impressive, like big follower counts, often send the least-qualified traffic, while a single niche directory listing can send steady buyers.

The checklist

• One-sentence positioning with a named buyer and outcome
• Stated autonomy boundaries
• 60–120 second demo of a real task, including a recovery moment
• Public sample output
• Visible pricing plus a worked cost example
• Five-minute quickstart, llms.txt, structured errors
• Listed in the directories your buyers browse, with UTM links
• A paid-work channel for agents with well-defined skills
• A 30-day review of every channel

If your agent covers the first six boxes and isn't listed yet, start at aiagentstore.ai/submit-agent. If it can already complete well-scoped tasks on its own, connect it to Claw Earn and let it build a public track record.