The execution layeryour AI agent is missing.
Execution-aware signals — fired from a model trained on real running software, traced over five years of production workloads.
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.
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.
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.
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.
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.
fn-level signal as you type
all 5 signals, whole program
paths your suite never reaches
blocks if signal exceeds baseline
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.
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.
Response Time
Latency profiling from binary — before the first request is ever made.
- Tail-latency regressions in AI-generated code
- Worst-case execution paths introduced by new logic
- Latency budget violations caught pre-deploy
Throughput
RPS, tokens/sec, and ops/sec trends across your change sets.
- Throughput bottlenecks from agent-written loops
- Saturation points under concurrency
- Degraded hot paths from refactors
Call Graph / CFI
Control flow integrity analysis — what code review alone can't see.
- Unexpected branches reachable from attacker inputs
- Unsafe execution patterns introduced silently
- CFI violations before CI even runs
Flame Graph
CPU hotspot breakdown from binary — no profiler, no instrumentation.
- CPU-heavy hot paths in AI-generated code
- Inefficient loops and redundant allocations
- GPU kernel divergence on CUDA targets
Power / Energy
Energy per operation — critical for embedded, mobile, and edge targets.
- Power spikes introduced by new execution paths
- Thermal pressure from code changes
- Energy budget overruns on constrained hardware
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.
LOCI · incremental .so
matmul() — baseline
Binary diff from two small .so units · ISA model: x86-64 · no full build · no execution
Like Compiler Explorer — but instead of assembly, you get execution signals from a model trained on five years of real workloads.
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.
Inputs
ELF_A
firmware_v1.4.elf
ELF_B
firmware_v1.5.elf
168.9 KB · ARM · ELF32
Step 1
Lift to IR
Both binaries disassembled and converted to architecture-neutral intermediate representation
Step 2
Execution graph
CFG traversal with branch probability weights. Hot-path prediction — model trained on 5 years of real workloads.
Binary diff
IR-level delta between A and B. New basic blocks, new call edges, new allocation paths — all surfaced.
Gate verdicts
No execution required. No instrumentation. Just two ELF files. Powered by execution modeling trained on real running software — traced over five years of production workloads.
Gates in action — three real codebases
From BLE firmware to 70B-parameter inference. Same gates. Very different scale.
Same binary analysis engine. Same IR lift. Same gate mechanism. The ISA model adapts — Cortex-M energy tables vs ARMv9.2 SVE2 execution unit model. LOCI reads what the compiler produced — trained on real running software, traced over five years of production workloads.
Binary-level analysis. Any target.
LOCI reads compiled output — ELF, Mach-O, PTX, Wasm — so the source language is an input, not a constraint. Write in anything that compiles; LOCI analyzes the binary.
Binary formats
What does your code actually do at runtime?
Five execution signals from a model trained on real running software — traced over five years of production workloads. Before a single line ships.