Automating AI Context: Strategies for Long-Running Agent Sessions

The promise of "1 million token context windows" is a trap. Just because you can stuff an entire novel into your prompt doesn't mean you should. As context grows, latency spikes, costs explode, and reasoning capabilities degrade (the "lost in the middle" phenomenon).

For autonomous agents running in production, you need active, automated context management. This guide covers the essential patterns to keep your agents lean and smart.

1. The Iteration Cap & User Confirmation Pattern

Infinite loops are the enemy of cost control. A simple but powerful pattern is to enforce a hard limit on autonomous steps before requiring human intervention.

The Strategy: Track the number of LLM calls (iterations) in a single session. Once a threshold is reached, pause execution and ask the user to make a decision: clear the context (start fresh) or summarize the history.

# Pseudo-code for Iteration Limiting
MAX_ITERATIONS = 10

class AgentSession:
    def __init__(self):
        self.history = []
        self.iterations = 0

    def run_step(self, user_input):
        if self.iterations >= MAX_ITERATIONS:
            return {
                "type": "control",
                "message": "I've been thinking for a while (10 steps). My context memory is getting full. Should I:",
                "options": ["Summarize & Continue", "Clear Context & Restart"]
            }
        
        response = llm.generate(self.history + [user_input])
        self.history.append(response)
        self.iterations += 1
        return response

This pattern prevents "zombie agents" from burning through your API credits while getting stuck in a reasoning loop.

2. Context Folding (Summarization)

When the context gets too long, don't just truncate the oldest messages. Fold them. Use a cheaper model (like GPT-4o-mini or Llama 3 8B) to summarize the first half of the conversation into a concise paragraph.

Example:
Raw History: [User: "Fix bug in auth.js", AI: "Checking code...", User: "Here is the error log...", AI: "I see the issue..."]
Folded Context: "System: The user and AI are debugging an auth issue in auth.js related to a 401 error."

3. Semantic Compression

Instead of summarizing everything, use Semantic Compression. Store conversation turns in a vector database. When a new query comes in, retrieve only the relevant past messages.

This "rag-over-history" approach allows an agent to "remember" a specific detail from 500 messages ago without cluttering the active context window with the 499 irrelevant messages in between.

4. Recursive Language Models (RLM)

For heavy data tasks (like analyzing a 500-page PDF), use a Recursive pattern. The agent shouldn't read the whole PDF. It should write a Python script to search the PDF for specific keywords, extract that text, and only read the extraction.

This turns the context management problem into a code execution problem, which is often cheaper and more accurate.

Conclusion

Automating context management isn't just about saving money; it's about reliability. A concise context leads to sharper reasoning. By implementing iteration caps and semantic compression, you build agents that can run indefinitely without degradation.