context-compression

Context Compression Strategies

When agent sessions generate millions of tokens of conversation history, compression becomes mandatory. The naive approach is aggressive compression to minimize tokens per request. The correct optimization target is tokens per task: total tokens consumed to complete a task, including re-fetching costs when compression loses critical information.

When to Activate

Activate this skill when:

• Agent sessions exceed context window limits
• Codebases exceed context windows (5M+ token systems)
• Designing conversation summarization strategies
• Debugging cases where agents "forget" what files they modified
• Building evaluation frameworks for compression quality

Core Concepts

Context compression trades token savings against information loss. Three production-ready approaches exist:

1. Anchored Iterative Summarization: Maintain structured, persistent summaries with explicit sections for session intent, file modifications, decisions, and next steps. When compression triggers, summarize only the newly-truncated span and merge with the existing summary. Structure forces preservation by dedicating sections to specific information types.

2. Opaque Compression: Produce compressed representations optimized for reconstruction fidelity. Achieves highest compression ratios (99%+) but sacrifices interpretability. Cannot verify what was preserved.

3. Regenerative Full Summary: Generate detailed structured summaries on each compression. Produces readable output but may lose details across repeated compression cycles due to full regeneration rather than incremental merging.

The critical insight: structure forces preservation. Dedicated sections act as checklists that the summarizer must populate, preventing silent information drift.

Detailed Topics

Why Tokens-Per-Task Matters

Traditional compression metrics target tokens-per-request. This is the wrong optimization. When compression loses critical details like file paths or error messages, the agent must re-fetch information, re-explore approaches, and waste tokens recovering context.

The right metric is tokens-per-task: total tokens consumed from task start to completion. A compression strategy saving 0.5% more tokens but causing 20% more re-fetching costs more overall.

The Artifact Trail Problem

Artifact trail integrity is the weakest dimension across all compression methods, scoring 2.2-2.5 out of 5.0 in evaluations. Even structured summarization with explicit file sections struggles to maintain complete file tracking across long sessions.

Coding agents need to know:

• Which files were created
• Which files were modified and what changed
• Which files were read but not changed
• Function names, variable names, error messages

This problem likely requires specialized handling beyond general summarization: a separate artifact index or explicit file-state tracking in agent scaffolding.

Structured Summary Sections

Effective structured summaries include explicit sections:

## Session Intent
[What the user is trying to accomplish]

## Files Modified
- auth.controller.ts: Fixed JWT token generation
- config/redis.ts: Updated connection pooling
- tests/auth.test.ts: Added mock setup for new config

## Decisions Made
- Using Redis connection pool instead of per-request connections
- Retry logic with exponential backoff for transient failures

## Current State
- 14 tests passing, 2 failing
- Remaining: mock setup for session service tests

## Next Steps
1. Fix remaining test failures
2. Run full test suite
3. Update documentation

This structure prevents silent loss of file paths or decisions because each section must be explicitly addressed.

Compression Trigger Strategies

When to trigger compression matters as much as how to compress:

Strategy	Trigger Point	Trade-off
Fixed threshold	70-80% context utilization	Simple but may compress too early
Sliding window	Keep last N turns + summary	Predictable context size
Importance-based	Compress low-relevance sections first	Complex but preserves signal
Task-boundary	Compress at logical task completions	Clean summaries but unpredictable timing

The sliding window approach with structured summaries provides the best balance of predictability and quality for most coding agent use cases.

Probe-Based Evaluation

Traditional metrics like ROUGE or embedding similarity fail to capture functional compression quality. A summary may score high on lexical overlap while missing the one file path the agent needs.

Probe-based evaluation directly measures functional quality by asking questions after compression:

Probe Type	What It Tests	Example Question
Recall	Factual retention	"What was the original error message?"
Artifact	File tracking	"Which files have we modified?"
Continuation	Task planning	"What should we do next?"
Decision	Reasoning chain	"What did we decide about the Redis issue?"

If compression preserved the right information, the agent answers correctly. If not, it guesses or hallucinates.

Evaluation Dimensions

Six dimensions capture compression quality for coding agents:

1. Accuracy: Are technical details correct? File paths, function names, error codes.
2. Context Awareness: Does the response reflect current conversation state?
3. Artifact Trail: Does the agent know which files were read or modified?
4. Completeness: Does the response address all parts of the question?
5. Continuity: Can work continue without re-fetching information?
6. Instruction Following: Does the response respect stated constraints?

Accuracy shows the largest variation between compression methods (0.6 point gap). Artifact trail is universally weak (2.2-2.5 range).

Practical Guidance

Three-Phase Compression Workflow

For large codebases or agent systems exceeding context windows, apply compression through three phases:

1. Research Phase: Produce a research document from architecture diagrams, documentation, and key interfaces. Compress exploration into a structured analysis of components and dependencies. Output: single research document.

2. Planning Phase: Convert research into implementation specification with function signatures, type definitions, and data flow. A 5M token codebase compresses to approximately 2,000 words of specification.

3. Implementation Phase: Execute against the specification. Context remains focused on the spec rather than raw codebase exploration.

Using Example Artifacts as Seeds

When provided with a manual migration example or reference PR, use it as a template to understand the target pattern. The example reveals constraints that static analysis cannot surface: which invariants must hold, which services break on changes, and what a clean migration looks like.

This is particularly important when the agent cannot distinguish essential complexity (business requirements) from accidental complexity (legacy workarounds). The example artifact encodes that distinction.

Implementing Anchored Iterative Summarization

1. Define explicit summary sections matching your agent's needs
2. On first compression trigger, summarize truncated history into sections
3. On subsequent compressions, summarize only new truncated content
4. Merge new summary into existing sections rather than regenerating
5. Track which information came from which compression cycle for debugging

When to Use Each Approach

Use anchored iterative summarization when:

• Sessions are long-running (100+ messages)
• File tracking matters (coding, debugging)
• You need to verify what was preserved

Use opaque compression when:

• Maximum token savings required
• Sessions are relatively short
• Re-fetching costs are low

Use regenerative summaries when:

• Summary interpretability is critical
• Sessions have clear phase boundaries
• Full context review is acceptable on each compression

Compression Ratio Considerations

Method	Compression Ratio	Quality Score	Trade-off
Anchored Iterative	98.6%	3.70	Best quality, slightly less compression
Regenerative	98.7%	3.44	Good quality, moderate compression
Opaque	99.3%	3.35	Best compression, quality loss

The 0.7% additional tokens retained by structured summarization buys 0.35 quality points. For any task where re-fetching costs matter, this trade-off favors structured approaches.

Examples

Example 1: Debugging Session Compression

Original context (89,000 tokens, 178 messages):

• 401 error on /api/auth/login endpoint
• Traced through auth controller, middleware, session store
• Found stale Redis connection
• Fixed connection pooling, added retry logic
• 14 tests passing, 2 failing

Structured summary after compression:

## Session Intent
Debug 401 Unauthorized error on /api/auth/login despite valid credentials.

## Root Cause
Stale Redis connection in session store. JWT generated correctly but session could not be persisted.

## Files Modified
- auth.controller.ts: No changes (read only)
- middleware/cors.ts: No changes (examined)
- config/redis.ts: Fixed connection pooling configuration
- services/session.service.ts: Added retry logic for transient failures
- tests/auth.test.ts: Updated mock setup

## Test Status
14 passing, 2 failing (mock setup issues)

## Next Steps
1. Fix remaining test failures (mock session service)
2. Run full test suite
3. Deploy to staging

Example 2: Probe Response Quality

After compression, asking "What was the original error?":

Good response (structured summarization):

"The original error was a 401 Unauthorized response from the /api/auth/login endpoint. Users received this error with valid credentials. Root cause was stale Redis connection in session store."

Poor response (aggressive compression):

"We were debugging an authentication issue. The login was failing. We fixed some configuration problems."

The structured response preserves endpoint, error code, and root cause. The aggressive response loses all technical detail.

Guidelines

1. Optimize for tokens-per-task, not tokens-per-request
2. Use structured summaries with explicit sections for file tracking
3. Trigger compression at 70-80% context utilization
4. Implement incremental merging rather than full regeneration
5. Test compression quality with probe-based evaluation
6. Track artifact trail separately if file tracking is critical
7. Accept slightly lower compression ratios for better quality retention
8. Monitor re-fetching frequency as a compression quality signal

Integration

This skill connects to several others in the collection:

• context-degradation - Compression is a mitigation strategy for degradation
• context-optimization - Compression is one optimization technique among many
• evaluation - Probe-based evaluation applies to compression testing
• memory-systems - Compression relates to scratchpad and summary memory patterns

References

Internal reference:

• Evaluation Framework Reference - Detailed probe types and scoring rubrics

Related skills in this collection:

• context-degradation - Understanding what compression prevents
• context-optimization - Broader optimization strategies
• evaluation - Building evaluation frameworks

External resources:

• Factory Research: Evaluating Context Compression for AI Agents (December 2025)
• Research on LLM-as-judge evaluation methodology (Zheng et al., 2023)
• Netflix Engineering: "The Infinite Software Crisis" - Three-phase workflow and context compression at scale (AI Summit 2025)

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Skill Metadata

Created: 2025-12-22 Last Updated: 2025-12-26 Author: Agent Skills for Context Engineering Contributors Version: 1.1.0