Agents: Inner Loop vs Outer Loop

2026-02-20 · Source: philschmid.de - RSS feed · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

AI agent frameworks operate on a hardcoded cycle: the model generates, executes tool calls if needed, feeds results back, and repeats until a text response is produced. This article clarifies that "closing the loop" in AI agents refers not to a special infrastructure loop, but to the model's learned behavior within this existing loop. The "inner loop" involves the agent verifying its own work during a single task, such as writing and running tests after code changes before delivering a final response. The "outer loop" concerns the agent's ability to learn and retain information across multiple user interactions or sessions, preventing repeated failures on similar tasks. This learning typically requires persistent state, skill files, or structured summaries, as most agents currently lack native cross-session learning capabilities.

Key takeaway

For AI Engineers designing or deploying agent systems, understanding the distinction between hardcoded loops and learned agent behavior is crucial. Focus on enhancing agent decision-making within existing infrastructure by implementing robust self-verification steps for the inner loop and developing persistent memory solutions for the outer loop. This approach will significantly improve task reliability and enable agents to learn and adapt across user sessions, reducing repetitive errors.

Key insights

Agent "loop closing" is about learned behavior within a hardcoded cycle, not new infrastructure.

Principles

• Agent loops are scaffolding, not behavioral.
• Verification improves single-task reliability.
• Persistent memory enables cross-session learning.

Method

Agents can verify work by calling tools (e.g., tests) within the existing generate-tool-call-feedback loop before responding, driven by system prompts or post-training signals.

In practice

• Implement system prompts for agent self-verification.
• Utilize AGENTS.md for persistent instructions.
• Generate SKILL.md to capture lessons from failures.

Topics

• AI Agents
• Inner Loop
• Outer Loop
• Agent Verification
• Persistent Memory

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

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• Loop Engineering: The Missing Governance Layer for Reliable AI Agents — Loop Engineering provides a dynamic, iterative governance framework for AI agents, ensuring reliability and control in complex, uncertain tasks.
• Why AI Agents Need to Check Their Own Work — Semantic control loops enable AI agents to self-correct and adapt by understanding the meaning of goals, not just numerical targets.
• A Practical Guide to Memory for Autonomous LLM Agents — Memory architecture, particularly its management, is more critical to LLM agent performance than model choice.
• You Shouldn’t Be Prompting AI Anymore. You Should Be Designing Loops. — The future of AI interaction is designing autonomous, verifiable loops, not just prompting agents.

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