The Loop Was the Easy Part: Evals, Observability, and Rollbacks for Your DIY Claude Code

2026-07-18 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

This article details how to operationalize DIY Claude Code-like agents built with LangChain's Deep Agents, focusing on crucial capabilities beyond the core agent loop: observability, evaluations, and rollbacks. It explains building a "flight recorder" using callback handlers like AgentTracer or Langfuse to track model and tool calls, token usage, and costs, alongside implementing step and token budgets (e.g., recursion_limit, TokenBudget). For evaluations, the post outlines creating EvalCase objects with deterministic and LLM-based graders to assess agent trajectory and outcome, enforcing quality with a regression gate in CI. Finally, it covers implementing two undo buttons: conversation time travel via LangGraph's checkpointer for /rewind and workspace snapshots (WorkspaceSnapshots) for file changes, especially when using LocalShellBackend with an approval gate.

Key takeaway

For MLOps Engineers deploying coding agents, prioritize building a robust operational harness around your agent loop. Implement comprehensive observability using callback handlers to monitor costs and behavior, and integrate token/step budgets to prevent runaway execution. Crucially, establish an evaluation suite with a CI-enforced regression gate to ensure prompt or tool changes don't silently degrade agent performance. Leverage conversation checkpointers and workspace snapshots, especially with approval gates, to enable reliable rollbacks and mitigate the risks of unintended agent actions on your codebase.

Key insights

Operationalizing AI agents requires robust observability, evaluation, and rollback mechanisms beyond the core agent loop.

Principles

• Agent dependability relies on surrounding harness, not just the model.
• Behavior changes from prompt edits need regression tests.
• Safety and reliability are enforced in the harness, not requested from the model.

Method

Implement observability via callback handlers for model/tool calls, token usage, and cost tracking, with step/token budgets. Evaluate agents using EvalCase with trajectory/outcome graders and a CI regression gate. Enable rollbacks with conversation checkpointers and workspace snapshots, ideally integrated with approval gates.

In practice

• Attach AgentTracer or Langfuse CallbackHandler to agent invoke config.
• Implement TokenBudget callback to prevent excessive token usage.
• Use WorkspaceSnapshots with approval gates for file change reversibility.

Topics

• LLM Agents
• Agent Observability
• LLM Evals
• Rollback Mechanisms
• LangChain Deep Agents
• MLOps Practices

Code references

• the-Sreejith/langclaude

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.