Compare the current and candidate setup
Run both on the same frozen repository work, with the same budget and acceptance checks. Repeat the cases where one clean pass could be luck.
PRIVATE BENCHMARKS FOR CODING AGENTS
Evaluate changes to your coding agent harness before rollout
Test changes to models, skills, MCP tools, instructions, permissions, and workflows on representative work from your private repositories. Fullbeam shows the evidence for where to consider promoting or restricting the candidate, and where to keep the baseline.
No evaluation infrastructure to build · Claude Code · Codex · OpenCode
Private preview: managed-native results are advisory shadow evidence. They cannot authorize or deploy a release.
Git records desired state. Fullbeam evaluates the change. Existing GitOps, MDM, vendor, and internal platforms distribute approved releases.
Current
backend-tools@17
Candidate
backend-tools@18
Example shadow decision
Backend maintenance
EVIDENCE SUPPORTS CANARY
Database migrations stay on the current release
Paired cases
18
Repeated coverage
82%
Regressions
1
Observed accepted-task cost
€8.10€5.02
Run the change before approving it for more teams and workloads
Fullbeam runs the current and candidate setup repeatedly on representative private repository work, verifies the outputs, and shows the evidence for which workloads may move, which should keep the baseline, and where the evidence is insufficient.
Make the decision by workload
Consider the candidate for promotion only where the evidence supports it. Restrict it, keep the baseline, or leave the result undecided everywhere else.
The complete coding-agent setup becomes a versioned Stack Release
Fullbeam records the model, harness, instructions, skills, MCP tools, permissions, verification, workflow, and runtime together, so every result points to the exact setup that ran.
This is HarnessOps
HarnessOps is the practice of versioning, qualifying, releasing, and governing the systems that produce software. Fullbeam provides its private evaluation and release-decision support layer.