50 lines
1.8 KiB
Markdown
50 lines
1.8 KiB
Markdown
# Agent Memory: Mental Models That Compound
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**June 9, 2026**
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The biggest problem with agents is they forget. Every session starts from zero. Mental models solve this.
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## What a Mental Model Is
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A YAML file that the agent owns. It reads it at startup and updates it after work:
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```yaml
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expertise:
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- topic: "API patterns"
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notes: "We use tRPC for type-safe API calls"
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evidence: "src/server/routers/*.ts"
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confidence: HIGH
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last_updated: "2026-05-24"
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observations:
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- type: failure_pattern
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observation: "WebSocket reconnection needs backoff"
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status: unaddressed
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```
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## The Rules
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1. **Agents own their mental models.** You do not touch them. The agent reads, writes, and updates its own expertise file.
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2. **Self-improve commands validate against the codebase.** The agent greps for evidence, checks if its knowledge is still accurate, and updates stale entries.
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3. **Read-only expertise for critical knowledge.** Billing workflows, deployment procedures, and security policies should never change.
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4. **Knowledge compounds across sessions.** Session 1: agent learns project structure. Session 2: learns common patterns. Session 3: learns failure modes. By session N, it operates at a senior engineer level for that codebase.
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## The Self-Improve Loop
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```bash
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just self-improve-backend
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```
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This command triggers the agent to scan the codebase, validate its expertise against actual file contents, and update anything that has drifted.
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## Why This Matters
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Without mental models, every agent session is Day 1. The agent rediscovers the same things repeatedly: "Oh, this project uses tRPC. Oh, tests go in the tests directory. Oh, we deploy via Docker." Mental models turn every session into Day 100.
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---
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*From Module 4 of the [Agentic Engineering Course](/). The full module covers multi-agent systems with domain locking, delegation, and P2P communication.*
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