Agents that work as you intend.
Counterfact is an agent quality harness that continuously redesigns and optimizes your AI system to be consistent, correct, and understandable, at the cost and latency point you choose.
For companies building AI products where consistency and correctness matter.
Most agent tooling records what happened and leaves the diagnosis to you. Counterfact engineers the system: modular components with defined responsibilities, root causes isolated to the component that produced them, fixes tested against the behavior they replace, and eval sets that keep the result in place. These are the disciplines software teams already trust, applied to a stack that has been running without them.
The graph is the front door. See the agents, the tools they call and the paths production takes, without reading the repository.
Counterfact writes the change, opens the PR in your repository and commits the eval set that proves it. One action, in your existing workflow.
Product owners read the same graph engineers do, so a question about intent gets answered by the person who knows, not translated first.
From one large prompt to a system you can understand and control
A flaky agent becomes a system you can inspect and rely on.
Counterfact brings the rigor of systems engineering to agent building: it decomposes a monolithic agent into discrete components, so you can see what each part is doing instead of guessing from one large prompt.
The component that caused the failure, found and fixed.
Counterfact tests interventions against the original behavior, identifying which change reverses the bad outcome and repairing it with measurable impact.
The router emits issue_refund and policy_check as parallel tool calls, so the refund lands before the policy result returns.
Everyone who shapes the agent can read it.
Counterfact asks the questions only a human can settle, and it asks them in a screen a product owner can read without opening the repository. Answers apply to the system in place. No prompt archaeology, no waiting for the one engineer who remembers how the router works.
Eval sets from a few labeled examples.
Good eval sets are expensive to build by hand. Counterfact takes a few labeled examples and generates a set that covers the behaviors you care about.
Quality, cost, and latency, at the point on the frontier you choose.
Counterfact compares combinations of models, providers, and system choices to trace the Pareto frontier of cost, quality, and latency, so you can run at the point that fits your budget and reliability target.
Counterfact keeps a consistent record.
Most teams stop after the first patch that seems okay. Counterfact records every evaluation, failure, and proven fix, so the system can change without losing what already works.
Reliability is a systems problem, not a model problem.
Counterfact Labs builds software for engineers who have shipped an AI agent and watched it fail in a way no dashboard explained. Agent reliability improves the way software reliability always has: by treating the system as a system that can be inspected, tested, and improved.
Our approach combines causal testing, structured evaluation, and production monitoring, so that every change is measured against the behaviors that matter to you and your users. We build the harness that keeps those behaviors working.
We have spent our careers building ML systems in production, and we have felt both the benefit of rigorous systems engineering and the difficulty of building reliable AI without it. That is why we built Counterfact.
Why we think this is a systems problem.
- Towards a science of scaling agent systems
- Intelligent AI delegation
- Prompts are software: the case for modular agents
- Iterative refinement of a multi-agent pipeline: a case study
- Beyond tracing: understanding multi-agent systems requires causal inference
- The missing discipline of production AI
- The shift from models to compound AI systems
Tell us what you are building, and where it fails.
Someone from the team will read it and get back to you.