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Managing Context Windows

Master how AI agents manage limited context windows to maintain coherent, efficient conversations

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Context Prioritization Strategies

Not all context is equally valuable. Prioritization decides what to keep when space is limited—choosing between recent messages, important facts, and critical system instructions.

Interactive: Strategy Comparison

Ranked messages (top = kept in context):
#1Discussing ML project requirements
Recency: 95%Importance: 95%1 turns ago
#2Weather is sunny today
Recency: 90%Importance: 10%2 turns ago
#3User said "thanks"
Recency: 85%Importance: 5%3 turns ago
#4Tool call: fetch_data()
Recency: 80%Importance: 80%4 turns ago
#5User name is Sarah
Dropped
Recency: 30%Importance: 90%15 turns ago
#6System prompt with instructions
Dropped
Recency: 20%Importance: 100%20 turns ago
Recency-Based: Prioritizes newest messages. Good for conversational flow, but may lose critical facts from earlier in the conversation.

🎯 Prioritization Factors

⏱️

Recency Score

Exponential decay: score = e^(-λ × turns_ago). Recent messages score higher. Typical λ = 0.1-0.3.

Best for: Conversational agents, chatbots
⭐

Importance Score

Manual tags: system=1.0, facts=0.9, tools=0.8, user=0.7, assistant=0.5, trivial=0.1. Or use LLM to classify.

Best for: Task-oriented agents, knowledge bases
🔗

Reference Score

How often is this message referenced later? High reference count = high value. Track with citation analysis.

Best for: Multi-turn reasoning, research agents
🧠

Semantic Relevance

Compute embedding similarity to current query. Keep messages most similar to active task. Dynamic prioritization.

Best for: RAG systems, context-aware agents

💡 Production Best Practices

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Always Keep System Prompts: System instructions should have importance = 1.0 and never be dropped. They define agent behavior.
•
Protect Tool Results: Tool outputs (API responses, database queries) are expensive and critical. Assign high importance (0.8-0.9).
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Combine Multiple Signals: Use weighted formula: score = (α × recency) + (β × importance) + (γ × relevance). Tune α, β, γ based on use case.
•
Monitor Drop Decisions: Log what gets dropped. If users complain about "forgetting," adjust thresholds or increase window size.
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