Out of the diff. Into the signal.
When the coding-agent session ends, Cockpit turns the change into engineering signals and one verdict — so your team reviews outcomes, not thousands of lines of code.
Three questions every change answers before merge.
Claude Code hands your team a thousand-line diff. Cockpit hands them the answer.
01
What changed?
Every function the diff touched — and what it does at runtime, not what it says in source.
02
What will it do when it runs?
Timing, power, memory and cache, predicted on-target before a single line ships.
03
Is it safe to ship?
One verdict against the budget — pass, caution, or pushback the agent must fix first.
Where Cockpit fits
Every tool manages one thing. Cockpit manages engineering judgment.
Instead of reviewing code, teams review engineering outcomes.
GitHub
manages source code
Jira
manages work
Datadog
manages production
Cockpit
manages engineering judgment
One screen. Four altitudes.
You run Cockpit in a second pane, right alongside your coding agent — Claude Code, Cursor or Copilot. Pick the workload closest to yours — OpenSSL cloud crypto or BLE embedded — then read it at the depth you need: raw timing, the value LOCI added, the supervision hours it removed, and the contract it holds the agent to.
CATCHES & CHECKS
CONTRACT ENVELOPE · cost vs budget ALPHA — loci-contract-…-openssl-token-svc.json
zero-tolerance (budget 0) — quality regressions the coding agent ships blind:
OpenSSL token-service session · p99 caught at PR
What LOCI caught on a crypto PR.
One backend engineer · OpenSSL token service · p99 regression caught at PR
Changes analyzed
Regressions caught pre-merge
First-pass clean on first measure
Supervision time avoidedestimated · ROI-grounded mean
Agent self-fixes shipped (LOCI → Claude)
Co-reasoning cycles
Worst regression caught
p99 EVP_encrypt 18× over budget — per-request alloc/free
Throughput protected
~22% under-load regression prevented
Zero-tolerance surfaced
1 — malloc/free on the hot path
Functions modeled
22 this session · cloud x86-64
Representative of the OpenSSL p99 case (see Case studies). Figures illustrate the caught regression; supervision hours are an ROI-grounded estimate.
From the terminal to the web.
The terminal is Cockpit at a single branch. On the web, it aggregates every session — across the team, and across time — so nothing an AI session learned is ever thrown away.
Review
Understand any change in 30 seconds.
The per-branch cockpit you just saw — execution signals and one verdict on every AI-generated change, in your terminal beside the agent.
You're looking at it.
Organization
Live engineering health, aggregated.
Every catch, contract and regression rolls up across teams and repos into one web view — so leads see where execution risk is concentrating before it ships, not after.
Aggregation across the org.
Knowledge
Engineering memory, never lost.
Every session, decision and measurement is captured and searchable. When a regression surfaces months later, the context that explains it is still there — the debugging trail AI sessions usually discard.
Sessions & data retained to debug.
Review the outcome, not the output.
Cockpit gives engineering teams a live view of every AI-generated software decision — before it reaches production.