Execution-Aware AI Platform

The execution layer your AI agent is missing.

Execution-aware signals — fired from a model trained on real running software, traced over five years of production workloads.

Workflow

One layer across your entire pipeline.

LOCI does not wait for a full build. It compiles incrementally — isolated object files per function or module — so signals are available from the moment code is written, not after CI finishes.

While you code

Incremental

As you type, LOCI compiles the current function or module into a small shared object (.so). No full build required — signals fire on the fragment you’re working on right now.

Like Compiler Explorer — always a binary, always a signal.

After full compile

Full binary

Once the build completes, LOCI runs a full binary pass — call graph, flame graph, response time, throughput, and power across the entire program.

Before tests run. Before CI queues.

During testing

Coverage gaps

LOCI maps which execution paths, tail cases, and edge scenarios your tests never reach. It doesn’t replace tests — it shows your agent the scenarios worth writing tests for.

Tail latency. Worst-case branches. Rare input paths.

Before merge

PR gate

A final signal check gates the PR. If any signal exceeds baseline — latency, throughput, power — the merge is blocked before it lands on main.

No surprises after merge. Ever.

LOCI SIGNAL LAYER

Plug in at

one stage

the full pipeline

Code

incremental .so

fn-level signal as you type

Build

full binary pass

all 5 signals, whole program

Test

tail & edge cases

paths your suite never reaches

Merge

full binary pass

all 5 signals, whole program

Each stage is independently useful — or run the full layer for continuous coverage.

HOVER A STAGE TO EXPLORE

Execution-aware agent

Your coding agent can now think with execution.

Your AI coding agent has no sense of how code actually executes — until now. LOCI gives it execution awareness: real signal data to plan features, resolve bugs, and gate CI before anything ships.

Planning a new feature

agent queries execution before writing

Agent queries the baseline

Before writing, the agent asks LOCI: what are the current response time, throughput, and power budgets for this system?

Plans within real bounds

Armed with execution data, the agent designs the feature within actual constraints, not hallucinated ones.

Signals validate the output

After the change compiles, LOCI confirms the new binary stays within baseline. The agent knows before you review.

First-pass ships clean

No rework. No regression surprise in PR. KPIs were baked in from the moment the agent started planning.

Investigating a bug

agent reads signals, not logs

Signal surfaces the anomaly

LOCI flags the deviation, a response time spike, power surge, or call graph branch that shouldn't exist. The agent sees it immediately.

Agent reads the flame graph

The agent gets the exact function, loop, or allocation responsible, from the binary. No log hunting. No reproduction needed.

Targeted fix, not exploration

Because the execution evidence is already in context, the agent's fix is precise. It's not guessing which path to try next.

Signal returns to baseline

LOCI confirms the anomaly is resolved before merge. The agent ships the fix knowing it worked, not hoping it did.

CI / Automated gate

signal diff on every PR, not just test pass/fail

PR opens — analysis triggers

LOCI binary analysis runs automatically in CI the moment a PR is opened. No manual steps. No configuration per repo.

Signals diff against base branch

Instead of pass/fail, CI gets a precise signal diff: response time +12%, throughput -8%, CFI clean. The agent sees exactly what changed.

Regression blocks merge

If any signal exceeds the defined budget, CI annotates the PR with the specific regression, not a vague failure. The agent knows what to fix.

Fix once, baseline updates

Once the agent resolves the regression, signals return to baseline, CI passes, and the new baseline is recorded for the next PR.

One layer, three workflows: plan features with execution bounds before writing, resolve bugs from signal evidence, and gate every PR with a precise signal diff — not a binary pass/fail.

Execution signals

Five signals. Zero guessing.

Each signal is a prediction from a model trained on five years of real running software — not logs, not sampling, not instrumentation. Fires before the code runs.

Free

Response Time

Latency profiling from binary, before the first request is ever made.

Developer

Throughput

RPS, tokens/sec, and ops/sec trends across your change sets.

Developer

Call Graph / CFI

Control flow integrity analysis, what code review alone can’t see.

Team

Flame Graph

CPU hotspot breakdown from binary – no profiler, no instrumentation.

Team

Power / Energy

Energy per operation – critical for embedded, mobile, and edge targets.

Incremental analysis

Signals fire as you write - or as your agent codes for you.

No full build required. Like Compiler Explorer — LOCI recompiles small units incrementally as code is written, lifts each to IR, and fires signals from the binary diff. Whether it’s you or Claude Code at the keyboard, the regression is caught before the function is finished.

Execution Gates

12 gates. Binary in. Pass or Block out.

LOCI lifts two binaries to IR, builds an execution graph, and fires or clears each gate from the diff, before a single instruction runs. No execution required. No instrumentation. Just two ELF files.

Where LOCI Fits

Earlier insight. Fewer surprises.

As you build your software: Code → Build → LOCI Signal → Test → Merge