How to Debug LLMs and AI Agents in 2026: Sandboxes, Plugins, and Copilots

How to debug LLM and AI agents today

When a language model produces an incorrect answer or an AI agent gets stuck in an infinite loop, pinpointing the root cause can be a lengthy and uncertain process. In 2026, debugging LLM and AI agents is no longer just about retrying with different prompts: thanks to runtime sandboxes, auto-fix plugins, and memory-based copilots, it’s possible to isolate problems, test repairs safely, and learn from each mistake systematically.

The challenges of modern AI agent debugging

Today’s agents operate across code, APIs, and user interfaces, multiplying the potential failure points. The most common issues include:

• Unmanaged API calls that cause crashes or loops.
• Hallucinations or non-factual responses that undermine reliability.
• Difficulty reproducing race conditions or transient states.
• Lack of historical context for continuous improvement.

1. Isolation and safety with runtime sandboxes

Anthropic releasedsandbox-runtime, a container-less, OS-level sandbox that rigorously limits file and network access for agents. Using it ensures that a debugging agent can never write outside a controlled work directory, preventing accidental damage.

Example of launching Claude Code inside the sandbox:

claude-code --sandbox \
  --workdir /tmp/debug_session \
  --allow-read ./inputs \
  --deny-write /etc \
  --deny-network

This command creates an isolated environment where you can run scripts, capture logs, and inspect stack traces safely.

2. Auto-fixing vulnerabilities with Cognition Devin

Cognition open-sourcedDevin Vulnerability Fixes, a plugin that uses three MIT-licensed skills to inventory, patch, and test PHP, Node.js, and self-managed Git code. The plugin integrates directly into the debugging workflow, offering immediate fix suggestions that you can approve or modify.

Typical workflow:

1. Rundevin-scan . --language phpto detect known vulnerabilities.
2. Let the plugin apply secure patches:devin-fix . --auto-approve.
3. Run the test suite:devin-test . --coverage.

The plugin logs each change in an immutable journal, providing an audit trail for security teams and developers.

3. Memory and state with Letta for persistent debugging

Letta offers a stateful copilot that maintains core memory, shared state, semantic recall, and custom tools. When debugging an agent, having persistent context lets you:

• View previous dialogue sessions.
• Reproduce specific conditions with identical input data.
• Test multiple fixes in parallel without losing state.

Example of using Letta to log a debugging session:

from letta import Session
session = Session("debug_agent_v1")
session.add_message(role="system", content="Interpret the following log: {{log}}")
session.add_artifact(path="./crash.log", type="log")
response = session.run(prompt="Which line of code caused the crash?")
print(response)

Practical workflow: from reproduction to fix

An end-to-end workflow that combines the three trends above can reduce time-to-resolution by over 80%.

1. Capture the problemstructlog) to record user inputs, agent calls, and errors.
2. Isolate the environment
3. Scan
4. Analyze with Letta
5. Propose and test
6. Iterate

Actionable tips and best practices

• Start with the sandbox: even the smallest prototypes should run inside sandbox-runtime by default.
• Version error logs: store each debugging session in a Git repository for future reference.
• Use the Devin plugin early: integrate vulnerability scanning into CI/CD to catch issues before they reach production.
• Leverage memory: spin up a Letta instance for each production service; cumulative context improves both debugging and performance.
• Document decisions: note why a particular patch was approved or rejected; this reduces repeat mistakes.

Conclusion

Debugging LLM and AI agents in 2026 is no longer about trial-and-error with prompts. By using runtime sandboxes, auto-fix plugins, and stateful copilots, teams can build secure, traceable, learning-based debugging pipelines. Start combining these technologies today and watch your agents’ quality improve with every correction cycle.