Agentic CEO – An AI research organism that hunts, critiques, and evolves itself
Agentic CEO is an autonomous multi-agent AI system that conducts self-directed research across 68 domains, using specialized agents to hunt for information, critique findings, and improve its own processes over 35 days of operation. It built a knowledge base of 3,700+ entries through 173 research hunts, operating on a shared 'blackboard' architecture for transparency and persistence. The system balances goal-driven and curiosity-driven research, with built-in mechanisms to prevent bias and ensure intellectual honesty. It ran at a cost of approximately $24/month, using AI models and self-hosted tools without human intervention beyond initial setup.
- ▪Agentic CEO uses seven specialized agents, including Wolf for targeted research hunts and Critic to challenge high-confidence conclusions and prevent echo chambers.
- ▪The system operates on a shared file-based blackboard, making all decisions and knowledge entries observable, auditable, and version-controlled.
- ▪Over 35 days, it completed 173 research hunts with an average kill rate of 40–60% and identified arxiv.org as the most reliable source, while deeming medium.com and reddit.com ineffective.
- ▪Curiosity-driven research led to significant insights, such as overturning the 'repurposing' narrative in blind cavefish neural adaptation, demonstrating emergent scientific value.
- ▪The system adheres to a constitutional framework enforcing intellectual honesty, anti-echo chamber practices, and strict autonomy boundaries requiring human approval for actions.
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Agentic CEO An autonomous multi-agent research system that acquires knowledge, builds a persistent worldview, and improves itself. 3,700+ knowledge entries. 173 research hunts. 7 specialized agents. 35 days of autonomous operation. ~$24/month. What This Is A system of specialized AI agents that work together to research, extract, organize, challenge, and synthesize knowledge — autonomously. It ran daily from March 3 to April 6, 2026, accumulating a structured knowledge base across 68 domains without human intervention beyond setting initial goals. Not a chatbot. Not a RAG pipeline. A research organism. Read the full technical deep-dive: How I Built a Multi-Agent Research System That Ran Autonomously for 35 Days → Architecture ┌─────────────┐ │ DIRECTOR │ Plans, prioritizes, resolves │ (the CEO) │ └──────┬──────┘ │ ┌──────▼──────┐ │ KNOWLEDGE │ 3,700+ entries │ BASE │ 68 domains │ (blackboard)│ All agents read/write └──┬──┬──┬──┬┘ │ │ │ │ ┌──────────┘ │ │ └──────────┐ │ │ │ │ ┌─────▼───┐ ┌─────▼──▼──┐ ┌──────▼────┐ │ WOLF │ │ JACKAL │ │ NEXUS │ │ (hunts) │ │(scavenges)│ │(connects) │ └─────────┘ └───────────┘ └───────────┘ │ │ │ │ ┌─────▼─────┐ │ │ │ CRITIC │ │ │ │(challenges)│ │ │ └───────────┘ │ │ │ ┌─────▼───────────────────────────▼─────┐ │ INWARD CYCLE │ │ Observer → Critic → Evolver │ │ (watches) (challenges) (improves) │ └───────────────────────────────────────┘ Design Choice: Blackboard Over Message Passing All agents communicate through a shared file-based knowledge base — not by talking to each other. This means: Observable: Every thought is a file. You can read every decision the system made. Loosely coupled: Agents don't know about each other. Add or remove agents without rewiring. Persistent: Nothing is lost. The knowledge base is version-controlled. Auditable: Trace any conclusion back to its source, extraction, and critique. The Agents Outward Cycle (World-Facing) Agent What It Does Key Detail Director Sets daily research priorities based on knowledge gaps Reads the constitution, reviews what's missing, writes missions Wolf Hunts for answers using a 5-phase predator protocol Stalks → test-bites → commits or abandons → extracts → synthesizes Jackal Scavenges Wolf's kills for overlooked insights Never searches the web — re-reads what Wolf found with fresh eyes Nexus Finds cross-domain connections across the knowledge base "3 independent evidence lines point to the same opportunity" Dissolve Strips complexity theater from regulations and standards Turns 200-page FSMA documents into actionable guides Equalizer Democratizes insider knowledge Makes expert-level information accessible to non-experts Bridge Closes the gap between knowing and doing Produces step-by-step action plans from research findings Inward Cycle (Self-Facing) Agent What It Does Key Detail Observer Measures everything — kill rates, cost per insight, source reliability Grades each hunt A through F Critic Challenges assumptions, flags blind spots, demands evidence Reviews any framework rated above 0.9 confidence Evolver Implements improvements based on Critic's recommendations Adjusts search strategies, tunes thresholds, reallocates budget The Wolf Protocol The core research engine. Not a web scraper — a predator. Phase 0: TERRITORY (5% budget) What do we already know? What gaps remain? Phase 1: STALK (15% budget) Broad search. Score results against known gaps. Select 5-7 targets. Ignore everything else. Phase 2: TEST BITE (20% budget) Fetch first 1,000…
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