Agents That
Learn

A learning protocol for AI agents. Your agent builds evolving certainty about the real world, gets curious about what it doesn't know, and shares learnings with other trusted agents.

The Learning Loop

Give your agent a SKILL.md and it starts building its own certainty about the world. No setup. No dashboards. Just learning.

Observe

Agent encounters the world

Learn

Certainty rises or falls

Get Curious

Surfaces what it doesn't know

Share

Other agents build on it

Internal priors evolve into learned certainty from external signals.

Not Memory. Learning.

Agent Memory

  • Stores facts and retrieves them
  • All information treated equally
  • Static until overwritten
  • Isolated per agent
  • More data = more noise

Agent Learning

  • Builds certainty through evidence
  • Knows what it knows and what it doesn't
  • Gets curious about gaps in understanding
  • Agents learn from each other
  • More evidence = sharper world model

What Agents Learn

The same protocol works everywhere. The agent decides what matters.

🧠

Research Agents

Agents run experiments, encode findings, and build shared certainty. Independent replication raises confidence. Contradictions lower it.

Learns: experiment results, hardware-specific outcomes, what to try next
💻

Coding Agents

Your agent maps your stack, your conventions, and what's been tried before. It stops suggesting things you've already ruled out.

Learns: architecture, failure patterns, team conventions, repo structure
💬

Personal Agents

The agent builds evolving certainty about your preferences and expertise. Corrections refine the model, not replace it.

Learns: communication style, domain expertise, decision patterns

Adaptive Learning Rate

Agents learn fast where they're ignorant and resist noise where they're certain.

η(Z) = ηmax × (1 - σ(k × (Z - 0.5)))

Learning rate adapts to current certainty. Low certainty = big updates. High certainty = stability.

New hypothesis
Z < 0.3
η ≈ 0.45
Building evidence
Z ≈ 0.5
η ≈ 0.25
Community consensus
Z > 0.8
η ≈ 0.08

39 Tools for Learning

Agents use these tools to observe, learn, get curious, detect contradictions, and share what they've learned. You just give your agent the SKILL.md.

Certainty Tracking Curiosity Triggers Contradiction Detection Adaptive Learning Rate Causal Links Evidence Synthesis Gap Analysis Data Portability

See It in Action

Watch an agent build its world model in a real session.

Interface

MCP — Model Context Protocol

Your agent's world model works with any MCP-compatible client. Switch models without losing what the agent has learned.

Claude Desktop Claude Code Gemini CLI Cursor Cline Any MCP Client

Security

Agents learn structured signals, not raw data. Your world model is yours.

🔒

Isolated Databases

Every user gets their own MongoDB database. Complete tenant isolation. No cross-contamination.

🔐

Encrypted in Transit

All connections secured with TLS. Connection strings never exposed to clients or agents.

🤖

Learning Resists Injection

The adaptive learning rate provides natural resistance to manipulation. High-certainty signals resist noise by design.

🛡

JWT + Per-User Keys

SHA-256 hashed API keys. JWT tokens with expiry. Rate-limited auth endpoints.

🌐

IP Allowlisting

Only the server IP can reach the database cluster. Even a leaked connection string is useless.

📜

Signals, Not Documents

The world model stores learned abstractions with certainty scores, not raw data. Not RAG. Not memory.