Your AI Agent Has Been Keeping Your API Key All This Time.

2026-06-23 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, long

Summary

The article details how AI agents, particularly those built with LangChain, can inadvertently leak sensitive data like API keys and lose operational coherence during extended sessions due to unmanaged context. It introduces two crucial LangChain middleware layers: `PIIMiddleware` and a combination of `SummarizationMiddleware` with `ContextEditingMiddleware` (specifically `ClearToolUsesEdit`). `PIIMiddleware` prevents sensitive data from reaching the model by masking, redacting, or blocking it, applying to both user messages and tool results. `SummarizationMiddleware` compresses older conversation history using a cheaper model (e.g., "anthropic:claude-haiku-4-5-20251001") when context usage hits 65%, preserving the 20 most recent messages. `ContextEditingMiddleware` removes raw tool outputs that the agent has already processed, triggering at 50% context usage and retaining the 8 most recent tool pairs. This middleware stack enables agents to run coherently for two hours, processing 45 files, a task that typically overwhelms basic agents in 40 minutes.

Key takeaway

For AI Engineers developing long-running agents, explicitly manage context to prevent API key leaks and maintain agent coherence. Implement a middleware stack with `PIIMiddleware` first for data redaction, followed by `SummarizationMiddleware` and `ContextEditingMiddleware` to prune irrelevant history and tool outputs. This structured approach ensures your agents can operate effectively for hours, avoiding silent degradation and costly restarts due to context overflow. Prioritize middleware order to guarantee sensitive data is handled before any summarization occurs.

Key insights

AI agents require explicit context management to prevent data leaks and maintain coherence over long sessions.

Principles

• LLMs use context windows, not memory; full history is resent.
• Unmanaged context causes data leaks and agent incoherence.
• Middleware order is critical for security and efficiency.

Method

Implement a middleware stack: `ModelCallLimitMiddleware`, `ToolCallLimitMiddleware`, `PIIMiddleware` (block API keys, mask credit cards, redact emails), `SummarizationMiddleware` (compress history at 65% context), and `ContextEditingMiddleware` (clear tool uses at 50% context).

In practice

• Configure `PIIMiddleware` for user messages and tool results.
• Use `SummarizationMiddleware` with a cheaper model for history.
• Prune old tool outputs with `ContextEditingMiddleware`.

Topics

• AI Agents
• LangChain
• Context Management
• PII Redaction
• Middleware
• Large Language Models
• API Security

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.