BearCrawl

Born from Alex’s OpenClaw security PRs (#2016, #10705), BearCrawl is a self-improving AI agent platform inspired by his daughter learning to bear crawl: agents observe, adapt, and improve in real time

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Inspiration

My daughter started learning to bear crawl. Watching her fall, adapt, and keep moving forward showed me what real learning looks like. That became the core idea behind BearCrawl: agents that do not just respond, they improve.

The technical spark came when I found 923 exposed AI agent instances on Shodan and contributed a security patch to OpenClaw (PR #2016, followed by PR #10705). That turned into a bigger mission: build a real AI operator, not a chatbot.

What it does

BearCrawl is an AI Chief Technology Officer platform.

Our primary agent acts as a true CTO for our company:

  • Runs operations across Slack, Discord, Telegram, iMessage, and WhatsApp
  • Maintains context across projects, priorities, and communication channels
  • Helps ship product, coordinate teams, monitor systems, and execute technical workflows
  • Operates continuously as both a strategic and execution layer

This is not a single assistant prompt. It is an always-on operating system for AI-native companies.

How we built it

  • Built on OpenClaw as the core agent runtime
  • Multi-agent architecture with role-based specialization:
    • Main CTO agent (core operator)
    • Community agent (public-safe communications and guardrails)
    • Domain agents (for specific workflows, like market analysis)
  • Cross-channel orchestration, memory, and context syncing
  • Observability and eval loops using Braintrust
  • Real-time docs support with Google DeepMind Developer Knowledge MCP
  • Voice layer with ElevenLabs

Sponsor tools integrated

  1. Google DeepMind MCP
  2. Braintrust
  3. ElevenLabs

Challenges we ran into

  • Context boundaries across channels and agent roles: we needed strong isolation for public-facing agents while preserving deep context for internal execution.
  • Safety vs autonomy: we designed guardrails so community-facing agents stay PR-safe while internal agents remain high-capability.
  • Operational reliability: keeping long-running agents stable across many tools and surfaces.

Results

  • A production-style AI CTO system running day-to-day company operations
  • Multi-agent role separation with clear public/private boundaries
  • Continuous improvement loop with measurable observability
  • Real-world workflows executed across messaging, product, and technical ops

Example use cases (tier-dependent)

  • Community management agent for public channels
  • Specialized market-research/trading agents
  • Project-specific agents for client operations
  • Personal executive assistant mode for founder workflows

Polymarket is one specialized example, not the core product.
The core product is the AI CTO platform that can spawn and manage these specialized agents safely.

What we learned

  • Companies need AI operators, not isolated chat assistants
  • Multi-agent role design matters more than model prompts alone
  • Guardrails and observability are required for real-world trust
  • Open-source contribution can directly evolve into product infrastructure

What's next

We are expanding BearCrawl into a full AI CTO platform where teams can deploy role-based agents with shared intelligence, controlled permissions, and continuous improvement built in.

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