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18/08/2026 2:49 pm
TL;DR: Security investigation agents break traditional context management because logs are verbose, anomaly-driven, and dependent on surrounding evidence, according to Cotool’s analysis of token estimation and pruning trade-offs. The practical lesson is that security AI needs fast, provider-aware measurement and conservative pruning, not generic RAG or sliding-window assumptions.
NHIMG editorial — based on content published by Cotool: Security AI context management and token pruning for agentic investigations
By the numbers:
- Using OpenAI's Tiktoken, encoding a 673K-token conversation takes about 368ms on an M4 MacBook Pro.
- On production GCP Cloud Run instances, encoding 678K tokens took 3.3 seconds.
Questions worth separating out
Q: How should security teams use AI in SIEM without losing identity context?
A: Security teams should use AI to accelerate correlation, summarisation, and triage, but only after identity telemetry is fully part of the detection pipeline.
Q: Why do AI agents struggle with security investigations more than document Q&A?
A: Security investigations depend on surrounding events, timestamps, and repeated patterns, so the meaningful signal is often distributed across a long transcript.
Q: What breaks when sliding-window context management is used for agentic security workflows?
A: Sliding windows can remove messages that still anchor reasoning, tool-call sequencing, or provider-specific message requirements.
Practitioner guidance
- Measure token usage per provider and message class Track token counts separately for user prompts, assistant text, reasoning blocks, and tool results so estimator drift is visible before investigations fail.
- Preserve investigation structure before pruning content Keep message ordering, tool-call pairing, and the latest assistant turn intact, then prune oldest and largest tool outputs first.
- Test context controls against long, noisy SIEM transcripts Use replayable investigations with verbose logs, repeated tool calls, and mixed provider responses to confirm the agent still returns correct results under pressure.
What's in the full article
Cotool's full post covers the operational detail this post intentionally leaves for the source:
- Benchmark results comparing token estimation methods across OpenAI, Anthropic, and Google providers.
- The exact weighted-estimation approach used to keep accuracy within the 110% overestimation threshold.
- Phase-by-phase pruning rules for tool results, assistant turns, and user messages in long investigations.
- Implementation lessons from extended-thinking requirements and AI SDK tool-call pairing constraints.
👉 Read Cotool's analysis of context management for security AI agents →
Security AI context management: are your agents pruning the right data?
Explore further
18/08/2026 3:31 pm
Context management is becoming a governance layer for security agents. Once an LLM is allowed to decide what to retrieve, the control plane is no longer just model selection or prompt design. It becomes a question of whether the agent can preserve enough evidence to support reliable investigation, auditability, and human review. For IAM and security operations teams, that means context loss is not a cosmetic defect but a control failure.
A question worth separating out:
Q: What should teams do when token limits are reached during an active investigation?
A: They should prune in phases, starting with the oldest and largest tool-result content while preserving the newest user and assistant turns. If the workflow uses multiple providers, the pruning policy must respect each provider’s message structure so the investigation can continue without corrupting state.
👉 Read our full editorial: Security AI context management fails when the model controls retrieval
