How to Fix Broken Agent Workflows

A broken agent workflow is one where a multi-step process stalls, loops, produces wrong results, or fails partway through. Because agents chain many steps together, a fault in any one of them can cascade into a confusing failure that is hard to diagnose from the final symptom alone. Fixing these problems is mostly a matter of isolating the failing step, understanding why it broke, and then hardening the handoffs between steps so the failure cannot recur silently.

Isolate the Failing Step

The first task is always to find where the workflow actually breaks, which is rarely obvious from the final error. Capture a full trace of a failing run that records the input and output of every step, every tool call, and the agent's reasoning along the way. Walk through the trace to find the first step that produces something wrong, since later failures are often just downstream consequences of an earlier mistake. Resist the temptation to fix the symptom you see at the end; the real bug is usually earlier in the chain, and patching the final step only hides it.

Repair Handoffs Between Steps

Many workflow failures occur not within a step but in the transitions between them. One step may produce output in a format the next step cannot parse, pass along an empty or malformed result, or omit information a later step depends on. Examine each handoff and verify that what one step emits matches exactly what the next expects. Add validation at these boundaries so a bad handoff fails immediately with a clear message rather than silently corrupting everything downstream. Standardizing the data structures passed between steps prevents a whole class of these mismatches and makes the workflow far easier to reason about.

Diagnose Logic and Control-Flow Problems

Some workflows break because the control flow itself is wrong. The agent may take a branch it should not, skip a required step, loop without making progress, or terminate before the task is complete. Review the conditions that drive each decision and confirm they reflect what you actually intend. For agents that decide their own next action, vague instructions often lead to erratic paths, so make the available steps, their preconditions, and the completion criteria explicit. Adding a clear definition of when the task is done prevents both premature stops and endless loops, two of the most common control-flow faults.

Add Recovery and Resilience

Even a correct workflow encounters transient failures, so design it to recover rather than collapse. Wrap each step so a failure returns a structured error the workflow can respond to, retrying transient problems and routing persistent ones to a fallback or a human. Persist progress between steps so a failure midway does not discard completed work and force a full restart. Cap retries and total iterations so a stuck workflow surfaces an error instead of running forever. These measures turn brittle pipelines into ones that bend without breaking under the inevitable imperfections of real systems.

Test the Whole Path, Not Just the Pieces

Once you have fixed a broken workflow, lock in the fix with a test that exercises the entire path end to end, including the specific case that failed. Because agents are non-deterministic, run the test several times and track its success rate rather than expecting a single pass. Build a small suite of representative and adversarial scenarios so future changes cannot silently reintroduce the same break. This end-to-end coverage catches handoff and control-flow bugs that unit tests of individual steps miss entirely.