Agentic Context Engineering

P0

Depth:

Designing, managing, and optimizing context for AI agents, treating context as a compiled view that determines agent capability, with tiered memory, summarization, and retrieval strategies.

Harness Layers

Meta

Meta (principles / narrative / research)

Prompt

Prompt (templates / few-shot / system instructions)

Orchestration

Orchestration (chaining / routing / looping)*

Integration

Integration (tools / RAG / external APIs)*

Guardrails

Guardrails (output validation / safety checks)

Memory

Memory (context / state / persistence)*

Eval

Eval (testing / metrics / iteration)*

4 of 7 layers covered

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Recommended entry points for exploring this thread.

Recommended start

Research Report 5.4: Shared Context & Memory

The distributed systems problem hiding inside every multi-agent AI system: how do agents share what they know without drowning in what they don't need?

Context Management System Architecture

How to architect context that scales across hundreds of agents without degradation

Research Report 2.3: State Management Across Calls

The stateless paradox: how conversational AI maintains the illusion of memory on protocols designed to forget everything

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