The hardest problem in building AI agents is not getting them to work once. It is understanding why they failed the second time.
Agentic workflows are stochastic by nature. A model call drifts. A tool invocation times out. A context window fills with garbage. The same prompt produces different outputs on consecutive runs. Developers stare at logs and guess. That is the state of the art in 2026, and it is not good enough.
Retrace is a new debugging tool that treats agent runs as deterministic replayable artifacts. It records every step of an agentic workflow, from the initial prompt through every tool call, model response, and state transition. Developers can then rewind to any point in the run, inspect the full context, and fork the execution from that moment. The name is literal: you retrace the agent’s steps.
The approach borrows from an older era of software engineering. Debuggers like GDB let developers set breakpoints, inspect memory, and step through code. Retrace applies the same model to agentic workflows, which are far less predictable than compiled code. The key insight is that recording is cheap and replay is deterministic, even when the underlying model is not.
What makes Retrace notable is the fork capability. When a developer spots the moment an agent went wrong, they can branch the run at that exact point, modify the prompt or tool configuration, and continue execution from there. This turns debugging into an iterative, branching process rather than a series of blind retries. It is the difference between fixing a bug by guessing and fixing a bug by reproducing it.
The tool arrives at a moment when agentic workflows are moving from prototypes to production. Companies like Salesforce, ServiceNow, and HubSpot have all shipped agent-based features in the past year. The economics of agents depend on reliability. A customer-facing agent that hallucinates a refund amount or books a flight to the wrong city erodes trust faster than any feature can build it. Debugging is not a nice-to-have. It is a prerequisite for the agent economy to scale.
Retrace competes in a space that includes LangSmith, Weights and Biases, and Arize AI, all of which offer some form of LLM observability. But those tools are built for monitoring and evaluation, not for interactive debugging. They tell you what happened. Retrace lets you step through why it happened and try a different path.
The product is listed on Product Hunt, which suggests it is early stage. The team has not disclosed pricing or a formal launch date. The core mechanic, replay and fork, is straightforward enough to describe in a sentence and powerful enough to change how teams build agents.
There is a deeper implication here. If agent debugging becomes deterministic, then agent testing becomes possible. Teams can build regression suites for agentic behavior, replay historical runs after a model update, and verify that fixes do not break other paths. That is the pattern that made continuous integration work for traditional software. Retrace points toward the same pattern for agents.
The open question is whether the recording overhead is acceptable at scale. Every agent run generates a trace of every model call, every tool output, and every state change. For complex multi-step agents, that trace can be large. Storing and indexing those traces for later replay requires infrastructure that most teams do not have. Retrace will need to solve the storage and retrieval problem as cleanly as it solves the replay problem.
Still, the direction is correct. AI agents will not become reliable through better models alone. They need the same debugging tooling that every other software discipline takes for granted. Retrace is a step toward that tooling, one replay at a time.