Long-Running Agents Keep Drifting Midway—The Problem May Not Be the Model

Long-running agents tend to drift after running for a while. The common reaction is to switch to a stronger model or add more context. DeepLearning.AI has just launched a free short course that focuses on another place: the loops outside the agent. The course is called Loop Engineering with LangChain Deep Agents, 1 hour 16 minutes, 8 videos, 4 code examples. The instructors are Sydney Runkle, product manager for LangChain Deep Agents, and Esmaeil Gargari, curriculum product lead at DeepLearning.AI. The course is free to enroll; the page notes that 2 graded assignments and a certificate of achievement are PRO.

The course first covers the basic agent loop: an LLM calls tools, observes results, and decides the next step. Then it adds four layers of loops. The agent loop handles execution; the verification loop has an evaluator check outputs against explicit criteria and retry with feedback if they fail; the event-driven loop connects the agent to Slack messages or Jira tickets so events trigger it; the hill-climbing loop analyzes traces from multiple runs, finds recurring problems, and then changes the agent’s harness. The course also introduces how LangChain Deep Agents use a file system, instructions, and skills to support longer tasks.

Someone in the comments added: set a hard cap on retries from the start. This reminder is not fancy, but it is crucial. A verification loop without a cap turns small errors into retry storms and bills. Another commenter said that loops plus trace-driven harness improvements are the way to make long-running agents truly reliable. Put the two views together, and the course’s focus becomes clear: reliability comes from engineering constraints, not from a longer prompt.

The course case study is a requirements-review agent that reads software specification documents and flags facts, ambiguities, assumptions, and clarification questions. People with a development background can view it as the skeleton of a requirements review agent; nontechnical readers can understand it as teaching the agent to first learn to find problems in a document before discussing automatic document revision.

Instructor pages:

The course does not solve the upper limit of model capability, nor does it guarantee that the agent won’t make mistakes. What it provides is an engineering framework for containing errors within loops. To avoid pitfalls, first write down four things clearly: retry limits, verification criteria, trigger entry points, and trace analysis. The free enrollment link is here, and the course page is here.