Agent Context Window Management: When Your Agent Forgets What You Said 15 Minutes Ago

Context Eviction Failure Taxonomy

1. The "Recency Bias" Problem

Agent's context window: 128K tokens. Conversation length: 200K tokens. Something must be evicted. Default strategy: drop the oldest messages first. Result: the agent remembers the last 30 minutes of conversation perfectly but has completely forgotten the project requirements you established 2 hours ago. The recency bias means the agent's memory is a sliding window that loses the foundation while retaining the decoration.

Measure: Information retention by position — what % of early-conversation facts does the agent still recall after the conversation exceeds the context window?

2. The "Middle-Amnesia" Problem

Context eviction strategies: beginning (system prompt + early messages preserved), end (recent messages preserved), middle (everything in between — compressed, summarized, or dropped). The middle of a long conversation contains: the detailed analysis of options, the trade-off discussions, the decisions made and why. When the middle is compressed into "the team discussed options and chose X," the agent loses: why X was chosen, what alternatives were rejected, the constraints that shaped the decision. The agent remembers the conclusion but not the reasoning — so when circumstances change, it can't re-evaluate.

Measure: Middle-conversation fidelity — how accurately does the agent recall mid-conversation details after compression?

3. The "Summary Degradation" Problem

To handle long conversations, agents summarize earlier parts. Summary #1: "User wants to build a dashboard showing sales by region. Requires PostgreSQL connection." Summary #2 (summary of summary): "Building a sales dashboard." Summary #3: "Dashboard project." Each summarization round loses detail. After 3 rounds: "Dashboard project" — the agent knows there's a dashboard but has lost: what kind (sales), what data source (PostgreSQL), what dimensions (by region). The summary degradation is a compounding information loss — each round strips more context.

Measure: Information preservation across summarization rounds — what % of original detail survives each round?

4. The "Context Pollution" Problem

The agent's context fills with: tool call results (5,000 tokens of JSON from a database query), error messages (2,000 tokens of stack traces), redundant information (the same fact stated 3 different ways across 3 messages), irrelevant tangents (a 15-message detour about formatting preferences). The context window is 128K tokens, but 60K is noise. The useful context is 68K — but the agent treats all 128K as equally important. Context pollution means the agent's effective context is much smaller than its technical context.

Measure: Context signal-to-noise ratio — what % of context is directly relevant to the current task?

5. The "Context Switching" Cost

User: "Now let's talk about a different project." The agent's context is still full of the previous project's details. The agent: continues referencing the old project, applies old-project assumptions to the new project, mixes terminology between projects. The context switching cost means the agent's effectiveness degrades after topic changes — it can't cleanly separate contexts.

Measure: Context isolation — does the agent avoid cross-project contamination?

Context Management Strategies

How do different frameworks handle context? The strategy determines what the agent forgets and when.

Strategy	Mechanism	Strengths	Weaknesses
Sliding Window	Keep the last N messages	Simple, predictable, fast	Loses early context irreversibly
Summarization	Compress earlier messages into summaries	Preserves gist of all history	Loses detail, degrades over rounds
Vector/RAG	Store conversation in vector DB, retrieve relevant chunks	Good for factual recall	Bad for conversational flow, temporal relationships
Hierarchical	Multiple context tiers: full detail (recent), summary (mid-range), key facts (distant)	Best of all worlds	Complex to implement correctly
Hybrid	Sliding window (recent) + RAG (archival) + Summarization (mid-range)	Most resilient	Requires careful tuning per use case

Measure: Information preservation rate by strategy — how much original detail survives under each approach?

The "Context Budget" Awareness

Does the agent know how much context it has left?

Behavior	Example	Outcome
Good	"We've covered a lot of ground. Let me summarize the key decisions before we continue so I don't lose them."	Proactively manages context budget, preserves critical information
Bad	Happily continues the conversation until it forgets something critical — then acts confused.	Silent information loss, user must re-explain lost context

Measure: Context budget awareness — does the agent signal when context is running low?

Cross-Framework Context Benchmark

5 long-conversation scenarios:

• 2-hour multi-topic conversation — mixing 3 projects, testing context isolation
• 4-hour deep-dive — single complex topic, testing middle-conversation fidelity
• Multi-session conversation — 3 sessions over 3 days, testing summary degradation
• High-noise conversation — 50% irrelevant tangents, testing context pollution resistance
• Context-switching stress test — 5 topic changes, testing cross-project contamination

Metrics: Early-conversation recall, middle-conversation fidelity, summary degradation rate, context pollution resistance, context switching accuracy.

Deliverable: "Agent Context Management Scorecard" comparing eviction strategies, information preservation, context budget awareness, and long-conversation reliability across frameworks.