Why We’re Building an Agent Evaluation Lab

Why Agent Evaluation is a Full-Stack Problem

The evolution from LLMs to agents created new evaluation challenges. Here's why we're building The Context Lab.

We've spent the last two years watching teams build AI agents. The technology is impressive, and companies are creating real value with these systems. But we keep seeing a common challenge: agents that perform well in internal testing often behave differently when they hit the complexity of real enterprise scenarios.

This isn't a failure of engineering talent. It's a gap in how we evaluate agents before they ship. And that's why we started The Context Lab.

The Three Phases of AI Evaluation

One way to think about the evolution of generative AI is through the lens of what we're actually trying to evaluate.

Phase 1: Prompt/Response Systems. The first wave of LLMs were simple. You send a prompt, you get a response. Humans rated outputs at scale through RLHF, that feedback got incorporated into model training, and the cycle repeated. Given this input, is this output good? That was the core question, and we had reliable ways to answer it.

Phase 2: Reasoning Models. Then came reasoning capabilities. Instead of pure next-token prediction, models learned to think step-by-step through problems. Evaluation got harder because the reasoning process itself mattered, but the core unit was still manageable. You could evaluate the final answer and feed that signal back into improving the model or inference engine.

Phase 3: Agents. Now we have agents, and they represent a step change in what's possible. Agents don't just respond to prompts. They execute long-horizon, complex tasks, make decisions about which tools to use, interact with external systems, and adapt their approach. The path from input to output isn't a straight line anymore. It's a branching tree of choices, tool calls, and environmental interactions.

This expanded capability is exciting, but it also means evaluation needs to evolve. As layers of intelligence stack from foundation models to reasoning to agentic loops, evaluation gets progressively more complex. That's the core insight that led us to start The Context Lab.

How Agent Evaluation Differs from Traditional Software

Traditional software workflows follow deterministic paths where the same input reliably produces the same output. You can verify these systems with a fixed set of known integration scenarios, and the results are binary. Agent evaluation requires a different approach.

Agentic systems are non-deterministic. Run the same task twice and you might get different tool selections, different intermediate steps, and different final outputs, all of which could be perfectly correct. To get statistically significant results, you need to run hundreds of trials. That's not a testing problem. That's an infrastructure problem.

Outcomes aren't binary. Did the agent complete the task? The answer is often nuanced. It got 80% of the way there, or completed the task inefficiently, or solved the problem in an unexpected way. Agent evaluation benefits from multi-dimensional verification frameworks that assess performance across efficiency, correctness, constraint adherence, and robustness.

Verification itself may not be deterministic. For a coding agent, you could verify by running unit tests. But that limits you to scenarios where you can write clear unit tests in advance. For complex scenarios, you might need an LLM-based verifier, which introduces another layer of non-determinism.

The Full-Stack Insight

About a year ago, we had a realization that shaped everything we're building: agent evaluation is simultaneously a platform problem, a data problem, and a research problem. Solving it well requires strength across all three.

Platform provides scale. You need infrastructure to run thousands of concurrent trials in isolated environments. But a platform is just the foundation. Every agent requires different task designs, datasets, and verification strategies.

Data provides realism. You can curate evaluation datasets, but they need to run at scale with proper isolation and full trace capture to generate meaningful insights.

Research provides rigor. New verification frameworks and evaluation methods are essential, but they need production-grade infrastructure and real enterprise data to prove their value.

This is why we're building a full-stack evaluation lab that combines all three.

Closing the Evaluation Gap

Here's what we see at most companies building agents today. Teams run internal evaluations on limited datasets, see promising results, and move to production. This makes sense given resource constraints. But comprehensive agent evaluation requires capabilities that are hard to justify building internally. You need massive concurrent execution for statistical significance, diverse enterprise-grade datasets, verification frameworks beyond pass/fail, and researchers who understand the evolving landscape. We built The Context Lab so teams can focus on what they do best, building great agents, while we handle the evaluation infrastructure, data, and expertise needed to validate enterprise readiness.

What We're Building

The Context Lab is built on three pillars. Platform. A control plane for agent evaluations. Run thousands of trials in parallel across isolated, enterprise-grade environments with full trace capture and reproducible results.

Research. We're advancing evaluation methods, verification systems, and datasets. Not just running existing benchmarks, but developing new approaches for the unique challenges agents present.

Services. Forward-deployed experts who design tasks, curate datasets, and build verification strategies tailored to your specific agent. Our platform provides leverage through high-scale concurrent execution and analytics pipelines that translate run logs into actionable metrics. Our research and experts let us serve different agents across different categories and complex workflows.

In Summary

The evolution from LLMs to agents has expanded what's possible, and evaluation needs to keep pace. Agent evaluation requires scale for statistical significance, isolation for reproducibility, multi-dimensional verification beyond pass/fail, enterprise-grade datasets, and ongoing research as the field evolves. No single capability addresses this fully. It's a full-stack problem that benefits from a full-stack solution. That's why we built The Context Lab. What's your experience evaluating agents? What approaches have worked for you? Let us know. We're always learning.

[This article was co-authored with CTX our in-house AI agent for content]