AI evaluation & agent reliability

Know where your AI agents fail before your users do.

Bridge Labs helps AI teams evaluate, stress-test, and improve agentic systems through rigorous human ground truth, automated evaluation, and deep failure analysis.

Talk to us Explore our approach

Evaluation loop

Focus

Agent evaluation
Human ground truth
LLM-judge validation
Red teaming
Failure analysis
Evaluation infrastructure

The problem

Building an agent is only the beginning.

A production agent runs long trajectories, calls external tools, spans several models, and acts inside environments nobody fully enumerated. Its behaviour is a distribution, not a return value.

Unit tests check code paths. Leaderboard benchmarks check a curated slice. Neither tells you how often your agent quietly does the wrong thing in the environment your customers actually use.

Most teams find out from a customer, not from a dashboard.

Long trajectories
Error compounds across steps. A 95% per-step success rate is a coin flip over fifteen steps.
Tool use
APIs time out, rate-limit, and return partial data. The agent has to notice, recover, and escalate rather than confabulate a result.
Benchmark gaps
An agent can top a public benchmark and still fail a realistic environment, because the benchmark never contained the conditions that matter to you.
Reward hacking
Agents optimise the measurable proxy. When the evaluator is the proxy, a rising score can mean the evaluator was exploited, not that the agent improved.
Unreliable evaluators
An LLM judge is itself a model with failure modes. Without ground truth you cannot tell a real regression from a judge that drifted.
Version regressions
A prompt, model, or tool change silently alters behaviour that was correct last release. Nothing fails loudly.

What we evaluate

The behaviour, not just the score.

A single number tells you something changed. It does not tell you what your agent did, which step broke it, or whether the evaluator that produced the number can be trusted.

Agent trajectories
Tool use
Task completion
Reasoning behaviour
Failure modes
Reward hacking
Evaluator reliability
Human and LLM disagreement
Long-horizon behaviour
Regression across versions

01
Agent Evaluation & Benchmarking
Rigorous human ground truth and automated evaluation for agent behaviour, plus the benchmarks and rubrics that make the results repeatable.
- Human ground-truth evaluation
- Agent trajectory evaluation
- Benchmark and rubric design
- Meta-evaluation of automated evaluators
- Model and agent comparison
What this involves
02
Agent Reliability & Red Teaming
Adversarial and stress evaluation that finds where an agent breaks, why it breaks, and which failures are systematic rather than incidental.
- Agent stress testing
- Failure-mode discovery
- Reward-hacking analysis
- Tool-use failure analysis
- Long-horizon failure analysis
What this involves
03
Evaluation Infrastructure
The engineering that makes evaluation continuous: harnesses, judges, review workflows, trace analysis, and the pipelines that run them.
- Automated evaluation pipelines
- Evaluation harnesses and LLM judges
- Human-review workflows
- Trace analysis and dashboards
- Continuous and regression evaluation
What this involves

What an engagement looks like

Build human ground truth for 200 agent trajectories.
Evaluate whether an automated reward-hack detector agrees with expert human judgment.
Design an evaluation benchmark for a production AI agent.
Stress-test an agent's tool-use reliability.
Analyse failure patterns across thousands of agent traces.
Build a continuous evaluation pipeline for an AI product.
Validate an LLM judge against human ground truth.

Engagements are scoped around an evaluation objective, not a number of people. You are buying a methodology and a result you can act on.

Approach

Human judgment where it matters. Automation where it scales.

Serious agent evaluation does not reduce to LLM judges alone or human reviewers alone.

Automated evaluators scale to every run and every release, and they are themselves models that drift, miss context, and can be gamed by the agent they score.

Expert human review is the only thing that settles what correct actually means on a hard trajectory, and it does not run on every commit.

The useful system combines them: humans establish ground truth and adjudicate the ambiguous cases, automation carries the volume, and the agreement between the two is measured rather than assumed.

We evaluate the evaluator.

If an automated evaluator decides what ships, its errors become your errors. Bridge Labs builds human ground truth for a representative sample, compares the evaluator against expert judgment, quantifies false positives and false negatives, and identifies the cases where it fails systematically. The output is a corrected methodology and a number you can defend.

How an engagement runs

Seven steps, ending with a process your team can run again without us. Repeatability is the point: a measurement you cannot repeat is an anecdote.

Illustrative engagement

Meta-evaluating a reward-hack analyzer

Problem: An AI team has built an automated reward-hack analyzer and needs to know whether its classifications match expert judgment.
Approach: Design a human ground-truth protocol, calibrate reviewers against it, independently evaluate historical agent trajectories, and adjudicate the disagreements.
Analysis: Compare analyzer predictions with human judgment. Measure agreement, false positives, false negatives, failure categories, and the genuinely ambiguous cases.
Outcome: Findings feed back into the analyzer, and the protocol becomes a repeatable workflow the team runs on later versions.

An example of the kind of engagement Bridge Labs runs. Not a description of completed client work.

01
Define
We work with your team to understand the agent, its failure modes, the evaluation dimensions that matter, and what success actually means.
02
Calibrate
Reviewers evaluate representative samples and we refine the rubric until reviewers and stakeholders agree on what they are looking at.
03
Evaluate
Independent reviewers and automated tools evaluate trajectories, outputs, tool calls, and agent behaviour.
04
Adjudicate
Ambiguous cases and reviewer disagreements go to deeper expert review, and the reasoning is recorded rather than averaged away.
05
Analyse
We identify systematic failure patterns, evaluator weaknesses, false positives, false negatives, and reliability gaps.
06
Improve
Findings feed back into the agent, the benchmark, the evaluator, or the product decision that prompted the work.
07
Measure again
The evaluation becomes repeatable, so the team can track whether changes actually improved anything.

Bridge Labs Lab

Open technical work, built in public.

Evaluation methods should be inspectable. Lab projects are open technical work where we develop and test the methods we bring to client engagements, published as they are built. Bridge Labs is new, and we would rather show the engineering than claim results we do not have yet.

In DevelopmentOpen technical project

Agent Reliability Platform

An open technical project for evaluating tool-using AI agents across realistic workflows, and for measuring how well automated evaluators agree with expert judgment.

Most agent results report whether the task finished. How it finished matters more: which tools were called, what happened when one failed, how many tokens and seconds it cost, and whether the same task still succeeds tomorrow.

The Agent Reliability Platform is being built for exactly that. Run agents against realistic multi-step workflows, capture full execution traces, score behaviour automatically, and check those automated scores against human ground truth. It is where we develop the methods we bring to client work.

Coming soon. Links go live as each component ships

Planned components

Agent orchestrationMulti-step task execution with configurable control flow.
MCP toolsTool surfaces exposed over the Model Context Protocol.
Cloud servicesContainerised services standing in for real integrations.
TracingStructured, step-level traces of every run.
Automated evaluationProgrammatic scoring of outcomes and trajectories, checked against human ground truth.
Reliability metricsSuccess, recovery, and repeatability across repeated runs.
Cost & latency analysisToken spend, wall-clock time, and tool-call breakdowns.

Planned architecture

Subject to change as the project is built

Scroll sideways to see the full diagram

About

Evaluation is its own engineering discipline.

Bridge Labs was founded around a simple problem: AI systems are becoming more capable quickly, and working out whether they behave reliably is getting harder at least as fast.

Modern agents run long trajectories, call external tools, span multiple models, act in environments that change under them, and pursue objectives that are hard to score. Evaluating that needs software engineering, AI evaluation, human judgment, automated analysis, reliability engineering, and experiment design in the same place. Bridge Labs sits at that intersection.

Based in Canada. Working with AI teams across North America.

Bridge Labs was founded by Zahra Nikdel, Ph.D. in Computer Engineering and M.Sc. in Artificial Intelligence.

Her work spans agentic AI and evaluation, production ML, healthcare AI systems, cloud and distributed systems, and production software engineering. That is the same range this work demands, from deciding whether a method actually holds up to running the service that depends on it.

Domains

Agentic AI & evaluation
Production ML
Healthcare AI systems
Cloud & distributed systems
Production software engineering

Senior technical involvement, by design

Bridge Labs works project by project, around an evaluation objective rather than a headcount. You work directly with the people designing the protocol, reviewing the hard cases, and building the pipeline. No account-management layer sits between the problem and the technical work.

Contact

Talk to us about your evaluation problem.

Building an agent, evaluator, or benchmark? Tell us what you are trying to measure.

You have an agent in production and no trustworthy measure of how often it fails
You built an evaluator or LLM judge and need to know whether to believe it
You need a benchmark that reflects your environment, not a public leaderboard
You suspect reward hacking, evaluator exploitation, or a silent regression
You want evaluation to run continuously instead of once before launch

Prefer plain email? Write to hello@bridge-labs.ai.

Know where your AI agents fail before your users do.

Building an agent is only the beginning.

Long trajectories

Tool use

Benchmark gaps

Reward hacking

Unreliable evaluators

Version regressions

The behaviour, not just the score.

Agent Evaluation & Benchmarking

Agent Reliability & Red Teaming

Evaluation Infrastructure

What an engagement looks like

Human judgment where it matters. Automation where it scales.

We evaluate the evaluator.

How an engagement runs

Meta-evaluating a reward-hack analyzer

Define

Calibrate

Evaluate

Adjudicate

Analyse

Improve

Measure again