Context Optimization Techniques

Context optimization extends effective capacity through strategic compression, masking, caching, and partitioning. Effective optimization can double or triple effective context capacity.

When to Activate

• Context limits constrain task complexity
• Optimizing for cost reduction (fewer tokens = lower costs)
• Reducing latency for long conversations
• Building production systems at scale

Core Strategies

Compaction

Summarize context contents when approaching limits, reinitialize with summary.

Priority for compression:

1. Tool outputs → replace with summaries
2. Old turns → summarize early conversation
3. Retrieved docs → summarize if recent versions exist
4. Never compress system prompt

Summary preservation by type:

• Tool outputs: Key findings, metrics, conclusions
• Conversations: Key decisions, commitments, context shifts
• Documents: Key facts and claims

Observation Masking

Tool outputs can comprise 80%+ of token usage. Replace verbose outputs with compact references once their purpose is served.

Masking Strategy:

Category	Action
Never mask	Current task observations, most recent turn, active reasoning
Consider masking	3+ turns ago, verbose outputs with extractable key points
Always mask	Repeated outputs, boilerplate, already summarized

Example:

if len(observation) > max_length:
    ref_id = store_observation(observation)
    return f"[Obs:{ref_id} elided. Key: {extract_key(observation)}]"

KV-Cache Optimization

Reuse cached computations across requests with identical prefixes.

Cache-friendly ordering:

1. System prompt (stable, first)
2. Tool definitions (stable)
3. Frequently reused elements
4. Unique content (last)

Design tips:

• Avoid dynamic content like timestamps
• Use consistent formatting
• Keep structure stable across sessions

Context Partitioning

Split work across sub-agents with isolated contexts. Each operates in clean context focused on its subtask.

Aggregation pattern:

1. Validate all partitions completed
2. Merge compatible results
3. Summarize if still too large

Budget Management

Design explicit token budgets:

• System prompt: X tokens
• Tool definitions: Y tokens
• Retrieved docs: Z tokens
• Message history: W tokens
• Reserved buffer: 10-20%

Trigger optimization when:

• Token utilization > 70%
• Response quality degrades
• Costs increase due to long contexts

Decision Framework

Dominant component	Apply
Tool outputs	Observation masking
Retrieved documents	Summarization or partitioning
Message history	Compaction with summarization
Multiple	Combine strategies

Performance Targets

• Compaction: 50-70% reduction, <5% quality degradation
• Masking: 60-80% reduction in masked observations
• Cache optimization: 70%+ hit rate for stable workloads

Guidelines

1. Measure before optimizing—know current state
2. Apply compaction before masking when possible
3. Design for cache stability with consistent prompts
4. Partition before context becomes problematic
5. Balance token savings against quality preservation