Why Memory Matters
Without memory, every session starts fresh. With memory, agents build on previous work. Memory enables:- Continuity — Pick up where you left off
- Learning — Accumulate knowledge over time
- Coordination — Share context between agents
Memory Structure
The Context Cascade
Memory isn’t a flat key-value store — it’s a layered cascade loaded before every agent execution, in strict priority order. When the token budget runs out, lower layers drop first, so an agent never loses its identity or mission, only the less-critical context underneath it:
Not every role loads every layer — scanners get strategy, goals, and their
own state (they discover, don’t decide); workers add feedback and active
work; leads and evaluators get all seven layers, since they need the
full picture to coordinate or judge output quality.
Memory Types
Agent State
Private to each agent ({squad}/{agent}/state.md):
- Task history
- Learned preferences
- Work in progress
Squad Memory
Shared across agents in a squad (goals.md, active-work.md, feedback.md):
- Measurable targets
- What’s already in flight
- The last cycle’s evaluation — what was valuable, what was noise
Managing Memory
Memory in Practice
When an agent runs:- Load — Retrieve relevant memory
- Inject — Add to agent context
- Execute — Agent works with full context
- Update — Store new learnings