Four teams. Four problems.One quality gate.
Under 5 minutes. No instrumentation. No runtime overhead.
Gate AI-generated code in firmware you can't afford to reflash.
WHAT YOU GET
Firmware leads, embedded software architects, and senior engineers shipping code to devices in the field, where a bad PR means a fleet-wide OTA rollback, a warranty claim, or a product recall.
THE PROBLEM
AI coding agents don't know your RTOS. They don't know your stack depth budget. They don't know that motor_ctrl() can't exceed 2KB or the whole system resets. Source-level linters and static analysis miss these constraints because they live in the binary, not the source.
WHAT LOCI DOES
Preflight audits the agent's plan against your binary-level budgets before code is written. Post-edit diffs the compiled binary against baseline and flags regressions on the PR. Stack depth, timing, power, and control-flow integrity, all enforced at merge, without instrumentation.
Anonymized pilot tier-1 automotive supplier
LOCI Gate — Post-Edit Review
⛔ Stack budget violated: motor_ctrl() now reaches 2,341 bytes (budget: 2,048)
Root cause: new helper validate_sensor_range() adds 293 bytes to worst-case path
Suggested fix: inline validate_sensor_range() or split into separate task
✓ Timing: within budget
✓ Power: no regression detected
Merge blocked. Approve to override.
Why existing tools miss it
- SAST / static analysis reads source. The overflow happens at link time, after inlining decisions.
- Coverity / LDRA / Polyspace run source-level checks. They can't see the final .elf.
- Runtime profilers fire after deployment. Too late.
- Stack-depth analyzers exist but require manual instrumentation and don't run on every PR.
/plugin install loci@claude-code-marketplace
Enforce MISRA and AUTOSAR budgets on every AI PR.
Who this is for
Functional safety managers, compliance leads, and QA heads in regulated industries, teams that must prove to an auditor that every change meets a numeric budget, every time, with traceable evidence.
THE PROBLEM
AI coding tools don't produce compliance artifacts. When an AI agent writes a change that affects a safety-critical path, your auditor wants to see: what was the baseline, what was the proposed change, what did the gate fire, who approved. Without that trail, AI-generated code can't be shipped in ISO 26262, DO-178C, IEC 61508, or similar regulated environments.
WHAT LOCI DOES
Every gate decision is logged, baseline binary, new binary, signal deltas, who approved, and when. Exportable as compliance evidence. Supports MISRA stack rules, AUTOSAR timing budgets, and custom per-function numeric constraints.
Anonymized pilot - industrial controls vendor
LOCI Audit Trail — PR #4,892
– Baseline: build-main-a1b2c3.elf (2026-03-14)
– Change: build-pr4892-d4e5f6.elf
– Gate: Post-Edit, budget set: AUTOSAR-T2
– Signals fired:
– Stack depth: sensor_poll() 1,840 bytes ≤ 2,048 budget ✓
– Timing: control_loop_hot() 312 µs ≤ 500 µs budget ✓
– CFI: no new indirect branches introduced ✓
– Human approver: [engineer-id]
– Approved: 2026-03-15 09:42:11 UTC
Why existing tools miss it
- Manual code review doesn't produce machine-readable evidence of budget compliance.
- Source-level compliance tools (LDRA, Polyspace) check rules but don't run on every AI PR.
- AI coding tools have no budget concept and no audit trail.
- Zero runtime overheaLOCI is the only gate that ties AI change + budget + human approval into a single traceable artifact.d, analysis on the compiled artifact
/plugin install loci@claude-code-marketplace
Catch throughput and latency regressions in BLE, TLS, and radio stacks, before they ship.
Who this is for
Connectivity and radio software engineers, teams shipping BLE stacks, TLS implementations, Wi-Fi drivers, and RF firmware where a 5% throughput regression is a customer escalation and a 10% regression is a product failure.
THE PROBLEM
AI coding agents confidently "optimize" crypto and packet-handling code without understanding the cost at the instruction level. A refactor that looks cleaner in source can add 3-8 milliseconds to a TLS handshake or cut BLE throughput 10%. These regressions are invisible to source-level review.
WHAT LOCI DOES
Post-edit diffs the compiled binary against baseline and predicts the delta on every critical path, handshake latency, packet throughput, per-operation energy — before the PR lands. No benchmark runs required. The signal fires at PR time, not at QA time.
Anonymized pilot - BLE SoC vendor
LOCI Gate — Post-Edit Review
⚠ Throughput regression detected: BLE throughput −8% in ble_ll_tx()
Root cause: memcpy() replaced with loop-based copy in new helper
Predicted impact: +1.2 ms per packet, −180 Kbps sustained
⚠ Timing regression: +3.4 ms in aes_ccm_encrypt() on the Connected-Secure path
Root cause: AES key schedule recomputed per call (new call pattern)
Suggested fix: cache schedule across calls in session context
Review evidence before merge.
Why existing tools miss it
- Benchmark suites don't run on every PR and rarely cover the regressed code path.
- Hardware-in-the-loop testing catches this in QA, weeks after merge.
- AI coding tools don't understand cycle cost or radio timing windows.
- LOCI catches it at PR time, from the binary, without running the radio.
npx skills add auroralabs-loci/loci-claude
Shift-left observability. Catch regressions before production.
Who this is for
Platform engineering, SRE, and infrastructure teams, organizations where latency and throughput are SLOs, not code-review preferences, and where catching a regression in production means an incident.
THE PROBLEM
Production observability (Datadog, New Relic, Grafana) catches regressions after they ship, during an incident, with customer impact. You can't gate a merge on a graph that won't exist until Monday. AI coding tools accelerate merge velocity, which compresses the window between bad code and bad graph.
WHAT LOCI DOES
Runs the same execution signal layer as your production APM, timing, throughput, tail latency, error paths, but fires at PR time, on the compiled binary, before merge. Set your production SLO as a budget; LOCI enforces it on every AI PR.
Anonymized pilot - infrastructure software vendor
LOCI Gate — Post-Edit Review
⚠ p95 latency regression: serialize_payload() +4.1 ms (p95)
Budget: p95 ≤ 15 ms per call
New p95: 17.3 ms — budget exceeded by 2.3 ms
⚠ Tail amplification: p99/p95 ratio increased from 1.4 to 2.1
Root cause: new fallback path fires under rare input distribution
Review before merge. Regression would hit production SLO within 48h of deploy.
Why existing tools miss it
- APM tools (Datadog, New Relic) fire after deploy. LOCI fires before merge.
- Load testing runs on a cadence. AI PRs don't.
- Static analysis doesn't model tail behavior or p95/p99.
- LOCI is shift-left observability, the same signals, fired earlier.
- uses: auroralabs/loci-action@v1 with: gate: post-edit fail-on-regression: true
AI writes code fast.LOCI keeps it safe to ship.
AI coding tools have compressed the gap between “written” and “merged” from days to minutes. What they haven’t compressed is the gap between “merged” and “regression detected”, that still takes production observability, customer reports, and on-call rotations. LOCI closes that gap at the binary level: every AI PR is reviewed against your execution budget before it merges. Human-on-the-loop. Evidence, not hope.
- Every AI-generated PR gets a binary-level review before merge
- Every regression is caught at PR time, not in production
- Every gate decision is logged and auditable
- Every approver is a human - LOCI surfaces evidence, you decide