Know which experiment is which
Every model, agent, tool, plugin, routing or context change gets its own fixed version. The parts you control are pinned. Hosted models can change behind the API, so every run records when it happened.
For AI Platform & Agent Infrastructure
Benchmark the system around the model
You changed a model route and tweaked a subagent, and now nobody is sure which change moved the results. How a model performs depends on the agent, tools, permissions, context, routing and checks around it. Fullbeam versions all of that together and benchmarks it as one setup.
In practice
What this looks like on your code
Every result links back to the setup, the tasks and the commit it came from. If something wasn't measured, you see that too, instead of a number that looks complete.
See what actually ran
Where a supported agent connection is installed, Fullbeam records the models, tools, subagents and fallbacks that actually ran and compares them with the setup you assigned. Without that connection, it shows Unknown.
Use your own mix of tasks
A generic coding benchmark misses your service boundaries, migrations and failure modes. Your merged PRs don't. Run repeated attempts on the work your teams actually do each week.
Don't let the system grade itself
Let the agent write a plugin or propose a route. Fullbeam turns that into a new setup and runs the same benchmark a human-made change gets, graded by your tests in a separate sandbox.
Got a coding agent change coming up?
An agent can suggest its next tool or workflow. Whether that change ships should depend on results from outside the system that suggested it.
Your current setupThe change you want to tryPRs you already mergedAn answer for each kind of work