Validate an LLM judge
Establish human ground truth, compare the judge’s decisions, and identify where it is too strict, too lenient, or missing context.
AI evaluation & agent reliability
Human ground truth, automated evaluation, and deep failure analysis. Bridge Labs helps AI teams test, measure, and improve their agents.
Agents act across tools, models, and changing environments. A passing score can hide failures that only appear in real tasks.
Evaluate the decisions, tool calls, and recovery steps that lead to an outcome. The final answer is only part of the evidence.
Establish human ground truth. Find where automated scores miss failures, reward shortcuts, or disagree with expert judgment.
Evaluate the whole agent.
For AI labs, agent startups, and engineering teams that need to understand whether their systems actually work.
- Agent trajectories
- Decisions, recovery, and behavior across a full task.
- Tool use
- Tool selection, call arguments, results, and error handling.
- Task completion
- Whether the intended outcome was reached, not just a high score.
- Failure modes
- Hallucinations, edge cases, reward hacking, and long-horizon failures.
- Evaluator reliability
- LLM judges, human disagreement, false positives, and missed failures.
- Version regressions
- What improved, what broke, and where changes shift behavior.
Evaluation.
Built around your system.
From defining what to measure to building the systems that measure it. Engage Bridge Labs around an evaluation objective.
Agent evaluation & benchmarkingWhat does good performance actually look like?
Design benchmarks and rubrics around your agent’s real tasks. Calibrate expert reviewers, evaluate full trajectories, and adjudicate disagreements to establish reliable human ground truth.
A documented evaluation protocol, reviewed dataset, agreement analysis, and comparisons across agents or versions. Validate LLM judges against human decisions, including their false positives and false negatives.
Example: build human ground truth for a set of agent trajectories, then measure how closely your automated evaluator agrees.
Agent reliability & red teamingWhere will the agent fail outside the benchmark?
Stress-test agent behavior across tools, environments, edge cases, and long-running tasks. Investigate hallucinations, reward hacking, evaluator exploitation, and unexpected behavior.
A failure taxonomy, reproducible failure cases, and targeted regression tests. Separate isolated mistakes from systematic weaknesses and identify what needs to change.
Example: test whether a tool-using agent completes the intended task or finds a shortcut that only satisfies the evaluator.
Evaluation infrastructureHow do you keep measuring as the system changes?
Build evaluation harnesses, LLM judges, human-review workflows, and trace-analysis pipelines. Connect your models, data, MCP servers, and tools to realistic evaluation environments.
Repeatable evaluation runs, versioned datasets, experiment tracking, and dashboards that expose regressions and failure patterns. Integrate results with your existing observability and development tools.
Example: build a continuous evaluation pipeline that compares agent versions before a release.
Human judgment.
Automated scale.
People establish what good behavior looks like and resolve ambiguity. Automated checks make evaluation repeatable across runs and versions. We design the two together.
Evaluate the evaluator.
Meta-evaluation tests whether your automated evaluator deserves your confidence. Compare its decisions with calibrated human ground truth, measure false positives and false negatives, and investigate systematic disagreement.
The result is a better understanding of what your scores mean, where they break down, and how to improve them.
Agent trajectory
- Task
- Tool calls
- Decisions
- Outcome
Human ground truth
Calibrated review
Expert adjudication
Automated evaluation
Checks and LLM judges
Repeatable measurement
Compare & investigate
Agreement. Missed failures. False alarms.
From a question
to reliable evidence.
Agree on the objective, calibrate the method, then investigate what the evaluation reveals. The scope is tailored to your agent, evaluator, or benchmark.
Define
Understand the agent, evaluation dimensions, likely failures, and success criteria.
Calibrate
Review representative samples and refine the rubric with your team.
Evaluate
Use independent human review and automated tools to assess traces, outputs, and tool calls.
Adjudicate
Resolve disagreements and ambiguous cases through deeper expert review.
Analyze
Find systematic failures, evaluator weaknesses, and reliability gaps.
Improve
Turn findings into changes to the agent, evaluator, benchmark, or product.
Measure again
Repeat the evaluation to track improvement and catch new regressions.
↳ A repeatable loop, not a one-time score.
Start with an
evaluation problem.
Example engagements. Each is an illustrative project scope, shaped around what your team needs to learn.
Meta-evaluate a reward-hack detector
Calibrate reviewers on agent trajectories, adjudicate disputed cases, and measure the detector’s false positives, false negatives, and systematic blind spots.
Benchmark a tool-using agent
Create realistic tasks and evaluation criteria, test tool-use reliability, and analyze how failures accumulate across long trajectories.
Build continuous evaluation
Connect trace collection, automated checks, human review, and regression analysis into a repeatable engineering workflow.
AI evaluation
meets engineering.
AI agents are becoming more capable. Understanding whether they behave reliably is becoming harder. Bridge Labs brings software engineering, human judgment, and automated analysis to that problem.
Our understanding of agent architecture, retrieval-augmented systems, and tool integrations helps us test behavior at the trajectory, environment, and system level.
We build the data pipelines, cloud systems, and analysis tooling that make evaluation an ongoing engineering discipline.
Built by people who have worked with teams at Google, Anthropic, OpenAI, and other frontier AI labs.
Based in Canada. Working with AI teams across North America.
Canadian talent.
Our work brings together writing, coding, subject knowledge, and careful human judgment.