Have an agent research its own competitors
Posted on May 13. Task: Posted on May 13 I built a self-hosted AI agent called Daemora. Last week I gave it one task: "Research your own competitors. Find their weaknesses. Build me a go-to-market strategy." I didn't touch it. Here's what came back. What I asked The full prompt was roughly: "You are Daemora. Research the personal AI agent landscape in 2026. Find the top competitors, analyse their pain points using real user complaints from Reddit, HN, and Twitter. Identify target customers. Write outreach scripts. Build a complete GTM strategy." No other input. No guidance. Just that. What it did It searched Reddit, Hacker News, Twitter, LinkedIn, Medium, and product review sites. Cross-referenced findings. Cited every source. Wrote a 3,000 word document with a full competitor analysis, 40 named target users with personalised pitches, and an outbound playbook. The whole thing ran while I was doing something else. What it found Pain point 1 — Memory 34% of all AI tool complaints analyzed across 500 Reddit posts came down to one thing: "It forgets everything every conversation." Every major competitor — OpenClaw, Lindy, CrewAI, LangGraph — has no persistent memory architecture. Every session starts from zero. Pain point 2 — Security This is where it got interesting. Daemora found CVE-2026-25253. A confirmed prompt injection and token theft vulnerability in OpenClaw, verified by Jamf Threat Labs. Users on HN describing it as a "stack of vulnerabilities." Reddit posts titled "Do Not Use OpenClaw." One user reported burning $300 in tokens in 2 days. The agent found this by itself. I didn't ask it to look for CVEs. It just followed the research trail. Pain point 3 — Token costs Managed SaaS agents are expensive and underdeliver.One Artisan AI user reported a 1% meeting booking success rate. 11x.ai was exposed on HN for claiming customers it didn't have. Lindy reviews described pricing that would "drain your wallet faster than a supermodel on a shopping spree in Paris." Pain point 4 — Setup complexity OpenClaw has half a million lines of code, 53 config files, and 70+ dependencies. One developer documented spending four weekends trying to get it running on a Mac Mini. What it recommended The agent identified 5 customer personas: The OpenClaw refugee burned by the CVE The indie hacker who wants ops handled while they build The privacy-conscious dev running Ollama locally The agency owner tired of $500/month SaaS bills The business needing research-as-a-service It wrote personalised DM scripts for each one. It identified 40 specific people to reach out to by name, with their Twitter handles and exact pitch angles. It cited every source with working URLs. Why this matters This isn't a demo I staged. I gave it a task. It used webSearch, webFetch, browserAction, writeFile, and createDocument autonomously. It decided what to search, which sources to trust, how to structure the output, and what conclusions to draw. The CVE discovery wasn't planned. It found that by following the research trail on its own. That's the difference between a chat tool and an agent. ChatGPT would have answered the question. Daemora went and found the answer. The irony The most compelling demo of what Daemora can do turned out to be Daemora researching itself. An AI agent that finds security vulnerabilities in its own competitors, identifies 40 target customers, and writes the outreach scripts — while you're doing something else. That's what we built. Try it yourself Top comments (0) For further actions, you may consider blocking this person and/or reporting abuse; tools: custom-built or unnamed agent. Human role: The original post does not say how a person stayed involved.; limitations: The source is a self-reported blog post with no third-party validation of the agent's findings or the accuracy of the cited CVE. The workflow assumes the agent has reliable access to real-time social data and can correctly interpret complex security disclosures. Human oversight was minimal during execution, raising risks of hallucinated data or biased analysis. The cost of running such an agent and the reliability of its autonomous decision-making are not quantified..
Tools
custom-built or unnamed agent
Published on Agent Work Atlas:
Overview
Posted on May 13
Workflow
Posted on May 13 I built a self-hosted AI agent called Daemora. Last week I gave it one task: "Research your own competitors. Find their weaknesses. Build me a go-to-market strategy." I didn't touch it. Here's what came back. What I asked The full prompt was roughly: "You are Daemora. Research the personal AI agent landscape in 2026. Find the top competitors, analyse their pain points using real user complaints from Reddit, HN, and Twitter. Identify target customers. Write outreach scripts. Build a complete GTM strategy." No other input. No guidance. Just that. What it did It searched Reddit, Hacker News, Twitter, LinkedIn, Medium, and product review sites. Cross-referenced findings. Cited every source. Wrote a 3,000 word document with a full competitor analysis, 40 named target users with personalised pitches, and an outbound playbook. The whole thing ran while I was doing something else. What it found Pain point 1 — Memory 34% of all AI tool complaints analyzed across 500 Reddit posts came down to one thing: "It forgets everything every conversation." Every major competitor — OpenClaw, Lindy, CrewAI, LangGraph — has no persistent memory architecture. Every session starts from zero. Pain point 2 — Security This is where it got interesting. Daemora found CVE-2026-25253. A confirmed prompt injection and token theft vulnerability in OpenClaw, verified by Jamf Threat Labs. Users on HN describing it as a "stack of vulnerabilities." Reddit posts titled "Do Not Use OpenClaw." One user reported burning $300 in tokens in 2 days. The agent found this by itself. I didn't ask it to look for CVEs. It just followed the research trail. Pain point 3 — Token costs Managed SaaS agents are expensive and underdeliver.One Artisan AI user reported a 1% meeting booking success rate. 11x.ai was exposed on HN for claiming customers it didn't have. Lindy reviews described pricing that would "drain your wallet faster than a supermodel on a shopping spree in Paris." Pain point 4 — Setup complexity OpenClaw has half a million lines of code, 53 config files, and 70+ dependencies. One developer documented spending four weekends trying to get it running on a Mac Mini. What it recommended The agent identified 5 customer personas: The OpenClaw refugee burned by the CVE The indie hacker who wants ops handled while they build The privacy-conscious dev running Ollama locally The agency owner tired of $500/month SaaS bills The business needing research-as-a-service It wrote personalised DM scripts for each one. It identified 40 specific people to reach out to by name, with their Twitter handles and exact pitch angles. It cited every source with working URLs. Why this matters This isn't a demo I staged. I gave it a task. It used webSearch, webFetch, browserAction, writeFile, and createDocument autonomously. It decided what to search, which sources to trust, how to structure the output, and what conclusions to draw. The CVE discovery wasn't planned. It found that by following the research trail on its own. That's the difference between a chat tool and an agent. ChatGPT would have answered the question. Daemora went and found the answer. The irony The most compelling demo of what Daemora can do turned out to be Daemora researching itself. An AI agent that finds security vulnerabilities in its own competitors, identifies 40 target customers, and writes the outreach scripts — while you're doing something else. That's what we built. Try it yourself Top comments (0) For further actions, you may consider blocking this person and/or reporting abuse
Reported result
The agent identified four key pain points: lack of persistent memory, security vulnerabilities (including CVE-2026-25253 in OpenClaw), high token costs, and setup complexity. It generated personalized DM scripts for five customer personas and listed 40 specific individuals to contact. The author claims the CVE discovery was unplanned, emerging from the agent's autonomous research trail, but provides no independent verification of the strategy's effectiveness or conversion rates.
Human role
The original post does not say how a person stayed involved.
Limitations
The source is a self-reported blog post with no third-party validation of the agent's findings or the accuracy of the cited CVE. The workflow assumes the agent has reliable access to real-time social data and can correctly interpret complex security disclosures. Human oversight was minimal during execution, raising risks of hallucinated data or biased analysis. The cost of running such an agent and the reliability of its autonomous decision-making are not quantified.
Try it
Act as a competitive intelligence agent. I will provide a list of [competitor names] and [target market]. Your task is to research their weaknesses and draft a GTM strategy. Use webSearch to find recent user complaints on Reddit, Hacker News, and Twitter. Use webFetch to verify specific claims, such as security vulnerabilities or pricing issues. Cross-reference findings to identify top pain points. Generate a 2,000-word report including: 1) Competitor analysis with cited sources, 2) Three distinct customer personas, 3) Personalized outreach scripts for each persona, and 4) A list of 10 potential leads with their public handles. Before finalizing, perform a dry run on one competitor and present the draft for my review. Do not send any messages or publish any content without my explicit approval. If data is missing or conflicting, flag it for human verification rather than guessing.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.