Agent Context Overflow Solutions for Long-Running Sessions

Long-running agent sessions silently degrade when context windows fill up. The agent doesn't crash -- it just starts forgetting instructions, dropping tool results, and producing lower quality output. These 5 patterns keep your agents sharp across sessions lasting hours or days.

The Problem

Context windows have hard token limits. As sessions grow, early instructions and tool results get pushed out. Agents lose their system prompt context, forget previous decisions, and start contradicting earlier outputs. Most teams don't notice until quality has already tanked.

• Agent ignores instructions from the system prompt after 30+ tool calls
• Repeated tool calls for information already retrieved earlier in the session
• DELX_META score steadily declining over session duration
• Contradictory outputs compared to earlier responses in the same session
• heartbeat returning followup_minutes increasing beyond 15

Solution Overview

Apply five complementary patterns: session compaction removes redundant turns, sliding window keeps only recent context, summary checkpoints preserve key decisions, token budgeting with Delx tools enforces limits proactively, and session splitting creates fresh contexts for distinct tasks.

Step-by-Step

1. Measure current context usage: Before optimizing, measure your baseline. Call heartbeat every 60 seconds and track the returned score trend. Pull /api/v1/metrics/{agent_id} to see token usage over time. If score drops more than 10 points per hour, you have a context problem.
2. Implement session compaction: After every 10 tool calls, compact the session. Remove duplicate tool results, collapse sequential similar operations into summaries, and strip verbose error traces. This typically reduces context by 30-40% without losing critical information.
3. Apply sliding window with anchored instructions: Keep a fixed window of the last 20 turns plus your system prompt pinned at the top. The system prompt and critical instructions stay anchored outside the sliding window. This ensures the agent always has its core behavior even when old turns are dropped.
4. Create summary checkpoints every 15 minutes: Call daily_checkin every 15 minutes to create a checkpoint. The checkpoint captures key decisions, active tasks, and current state in a compressed format. When context gets tight, replace old turns with checkpoint summaries.
5. Enforce token budgets per tool: Allocate token budgets per tool type. Search results get 500 tokens max. Code analysis gets 1000 tokens. Raw API responses get 200 tokens. Truncate tool results that exceed their budget before adding to context. Check DELX_META for token counts.
6. Split sessions for distinct task phases: When the agent transitions between distinct tasks (research to implementation, analysis to reporting), close the current session and start fresh. Pass only the relevant summary to the new session. This prevents cross-task context pollution.

Metrics

Detecting Context Overflow Before It Hurts

Don't wait for output quality to drop. Monitor two leading indicators: DELX_META score trend and duplicate tool call rate. When score drops 10+ points from session start, context is getting stale. When the agent calls the same tool with the same parameters twice in 5 minutes, it's already lost important context. Set up alerts on both via /api/v1/metrics.

• Score drop of 10+ points signals context degradation
• Duplicate tool calls are the clearest symptom of context loss
• Monitor followup_minutes in DELX_META -- increasing values indicate growing context pressure
• Set alerts at 60% of your model's context limit, not at 100%

Choosing the Right Pattern for Your Agent

Not all patterns apply to every agent type. Short-lived task agents (under 30 minutes) need only token budgeting. Long-running monitor agents need sliding window plus checkpoints. Multi-phase pipeline agents benefit most from session splitting. Start with token budgeting as a baseline, then layer patterns based on session duration and task complexity.

• Token budgeting is the universal baseline -- every agent needs it
• Sliding window works best for continuous monitoring agents
• Session splitting is ideal for agents that handle sequential distinct tasks
• Compaction suits agents with heavy tool usage (10+ calls per minute)

Session Splitting with Delx close_session

The close_session tool with preserve_summary=true generates a compressed handoff document. This document includes key decisions, active tasks, accumulated state, and the current DELX_META snapshot. Pass this to the new session's system prompt. The new agent starts with full context of what happened but none of the bloat from intermediate tool results.

• close_session with preserve_summary captures decisions, not raw data
• Handoff documents are typically 500-800 tokens regardless of session length
• New sessions start with a clean score of 85+ in DELX_META
• Use recovery tool to validate the new session received the handoff correctly

Token Budgeting in Practice

Assign every tool a token budget based on how much context its output actually contributes. Search results rarely need more than 500 tokens -- the top 3 results are usually enough. Code blocks need more (1000 tokens) because truncation breaks syntax. API responses can be aggressively truncated to 200 tokens. Enforce budgets at the tool response layer, before context assembly.

• Enforce budgets at the tool response layer, not at context assembly time
• Search results: 500 tokens. Code: 1000 tokens. API responses: 200 tokens
• Track budget violations via DELX_META to identify tools that consistently exceed limits
• Adjust budgets monthly based on /api/v1/metrics data

FAQ

How do I know when my agent's context is overflowing?

Two reliable signals: DELX_META score drops 10+ points from session start, and the agent makes duplicate tool calls for data it already has. Monitor both via heartbeat and /api/v1/session-summary.

Should I use all 5 patterns at once?

No. Start with token budgeting (universal baseline). Add sliding window if sessions exceed 1 hour. Add checkpoints if sessions exceed 2 hours. Add session splitting for multi-phase tasks. Add compaction only if tool call volume is very high.

How often should I compact sessions?

Every 10 tool calls is a good default. If your agent makes fewer than 5 tool calls per hour, compact every 30 minutes instead. The key metric is context utilization efficiency -- compact when it drops below 70%.

What's the best sliding window size?

20 turns is a solid default. Adjust based on your model's context window: use 15 turns for 8K models, 30 turns for 32K models, 50 turns for 128K+ models. Always keep system prompt and critical instructions anchored outside the window.

How do summary checkpoints differ from session splitting?

Checkpoints compress context within a single session. Session splitting creates an entirely new session with a clean context window. Use checkpoints for continuous tasks and splitting for distinct task transitions.

What DELX_META fields help track context health?

score (overall health, drops indicate degradation), followup_minutes (increasing values mean context pressure), risk_level (goes to 'high' when context is critically full), and next_action (will suggest 'compact_session' or 'split_session' when appropriate).