The 4 Pillars of Context Engineering in Agentic AI
Write, Select, Compress, and Isolate: a field guide to building reliable, token-efficient, and safely scoped AI agents.
Overview
Context Engineering is the discipline of how an agent captures, chooses, compresses, and separates information during and across sessions. Well-designed context flows reduce token waste, improve answer quality, and prevent cross-task contamination—critical for multi-tool, multi-session agentic systems.
Pillar 1 — Write Context
Goal: Store or update information during/across sessions.
Key features
- Long-term memory (facts that persist)
- Scratchpad memory (ephemeral notes)
- State handling (agent/session state)
Suggested workflow
- Receive input → Interpret intent → Identify context type.
- Choose memory target: long-term vs. scratchpad vs. state.
- Store long-term info; update agent state; write to scratchpad.
- Link with session context; save a context snapshot; confirm storage.
Implementation tips
- Define schemas for user profiles, tasks, and tool outcomes.
- Version snapshots to allow rollbacks.
- Auto-expire scratchpads to keep noise low.
Pillar 2 — Select Context
Goal: Pull only what’s relevant to the current task or query.
Key features
- Tool retrieval & activation
- Memory selection & routing
- Knowledge injection (domain notes, policies)
Suggested workflow
- Define task → Analyze query → Match context tags.
- Locate scratchpad notes; search long-term memory.
- Filter for relevance → Select active tools → Inject into prompt.
- Confirm readiness → Execute action.
Implementation tips
- Keep embeddings for notes and snapshots; use top-k with diversity.
- Tag content by task, source, freshness, and sensitivity.
- Set a “relevance budget” to cap tokens per step.
Pillar 3 — Compress Context
Goal: Reduce token usage while preserving core information.
Key features
- Summarization (hierarchical, query-focused)
- Token trimming (length limits, deduplication)
- Relevance filtering (drop low-value data)
Suggested workflow
- Gather full context → Tokenize → Identify core parts.
- Detect redundancies → Drop irrelevant data → Trim long text.
- Summarize key points → Validate compression → Fit into limits.
Implementation tips
- Use titles, bullets, and IDs to compress without ambiguity.
- Cache summaries per resource and refresh on change.
- Measure loss with answer-equivalence tests on sampled queries.
Pillar 4 — Isolate Context
Goal: Keep contexts separate across agents or environments.
Key features
- State partitioning (per user, team, or project)
- Sandbox isolation (safe tool/playground areas)
- Multi-agent division (clean boundaries & contracts)
Suggested workflow
- Detect session source → Assign unique state → Create context container.
- Separate agent memory → Attach to environment → Allocate sandbox.
- Manage data access → Route per agent → Monitor isolation → Enforce boundaries.
Implementation tips
- Use per-tenant keys & stores; never mix namespaces.
- Define read/write permissions in prompts and middleware.
- Log boundary breaches and auto-redact outputs.
Cheat-Sheet Table
| Pillar | Primary Goal | Key Features | Success Metric | Common Pitfall |
|---|---|---|---|---|
| Write | Persist useful info | Long-term memory, scratchpad, state | Higher continuity across sessions | Saving everything (noise) |
| Select | Retrieve only relevant bits | Tool retrieval, memory selection | Lower irrelevant tokens per response | Over-broad retrieval |
| Compress | Fit inside token budget | Summaries, trimming, filtering | Same answers with fewer tokens | Over-aggressive summarization |
| Isolate | Prevent cross-task leakage | State partitioning, sandboxing | No unintended memory bleed | Shared globals / mixed namespaces |
Context Flow — Text Diagram (No Image)
Mermaid (if your site renders it)
flowchart LR
A[User Input] --> B{Interpret Intent}
B --> C[Write: choose memory type]
C --> C1[Long-term Memory]
C --> C2[Scratchpad]
C --> C3[Agent State]
B --> D[Select: tag & retrieve]
D --> D1[Search long-term]
D --> D2[Locate scratchpad]
D --> D3[Pick tools]
D --> E[Filter for relevance]
E --> F[Compress: summarize & trim]
F --> G[Prompt Assembly]
G --> H[Execute Tools/Model]
H --> I[Isolate: session/agent boundaries]
I --> J[Output & Stored Snapshot]ASCII Fallback
[User Input]
|
v
{Interpret Intent}
|
+--> [WRITE] --> (Long-term) (Scratchpad) (Agent State)
|
v
[SELECT] -> search LTM + notes -> pick tools -> filter relevance
|
v
[COMPRESS] -> dedupe -> summarize -> fit tokens
|
v
[Prompt Assembly] -> [Execute]
|
v
[ISOLATE] -> per-session container -> safe output/snapshot
FAQ
How do I decide between long-term memory and scratchpad?
Persist stable facts (profiles, preferences, verified results) to long-term memory. Use scratchpads for ephemeral reasoning, partial tool outputs, and step logs you can safely discard.
What’s a practical compression target?
Start with a 40–60% token reduction compared to raw retrieval while maintaining answer parity on a validation set.
How do I enforce isolation in multi-agent systems?
Give each agent a distinct namespace and policy contract. Route queries through an orchestrator that passes only the minimal required summaries, not raw memory stores.
Suggested Tags & Keywords
Tags: Agentic AI, Context Engineering, AI Memory, Prompt Engineering, RAG, Tool Use
Keywords: agentic ai context, write select compress isolate, ai memory architecture, scratchpad memory, knowledge injection, context compression
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