Close Cookie Popup
Cookie settings
We use cookies to run the site and understand how it’s used. You can accept all cookies or reject non-essential ones. See our Cookie Policy.
Manage settings
Close Cookie Preference Manager
Cookie settings
By clicking 'Accept all cookies', you agree to storing cookies on your device to enhance site navigation, analyze site usage and assist in our marketing efforts as outlined in our cookie policy.
We make your software engineering agents measurably better.
Code review, bug fixing and prevention, incident response: your agents, scored on real outcomes and optimized against them.
The problem
Agentic Software Engineering feels like it works, but productionizing it remains elusive.
How do you measure your agents' performance? Most organizations know they should but not how to do it. We bring the experience from years of research and running agents in production to your organization.
Starting out.
First agentic workflows are taking shape. Build with evaluations from day one; skip the trust crisis later.
Agents in production.
PR counts go up, but when a model update breaks your agents overnight, you find out from the bill or customer complaints.
Ready for full automation.
Agents already do the work, but an engineer still checks every outcome. Focus their time where it's needed.
Agentic engineering
From running agents to engineering them: benchmark, improve, monitor.
Two connected parts: a one-time engagement that puts benchmarks in place and optimizes your agents, and a platform that monitors your agents over time.
The Engagement
time-boxedOne agent at a time: benchmark, improve, monitor, more trust and less oversight. Works with the agents you already run; if you don’t have an autonomous coding agent yet, we establish the first one with you. Typically this starts with PR review.
01
Historic Analysis
How are your agents doing? How much human oversight is actually needed? What are their failure modes?
access: read-only
02
Live Monitoring
Surface performance degradation and cost spikes as they happen.
access: repo events
03
Historic Benchmark
Your history, replayable. Optimize your agent's cost and capability with confidence.
access: plus agent setup
04
Agent Optimization
Feedback becomes instructions. Less oversights, where it's not needed.
access: plus agent access
Your side
One counterpart engineer, a few hours a week, and your agent team for two half-day workshops.
Our side
CTO-led scoping workshop, the forward-deployed engineer, the tooling, and everything else.
NDAyour environment, your keysyour IP: benchmarks, labels, prompts
The Platform
ongoingEvery code agent you run is monitored: produce results, observe real-world outcomes, find issues and suggest improvements. Compare different agent versions side by side: testing a new harness, a new model, or a new MCP? Evaluate before using it in production.
What you get
A live answer to “are the agents doing their job?”
review analysis / code-review agent
illustrative
Defect coverage
61%
of defects that later needed a fix were flagged in review
Fix match rate
44%
of findings matched by a fix in a later commit
Escaped defects
12
merged undetected this period, 3 critical or high
Blind spots
3
recurring issue clusters the review agent never flags
The platform dashboard (illustrative data).
Defect coverage Escaped defects Blind-spot clusters Precision and recall Cost and waste Regression detection
Ground truth comes from your SDLC and your engineers, not from an LLM grading its own homework.
Who this is for
Teams automating real engineering work.
Starting now
We establish the first agent with you, evaluation wired in from day one.
Agents in production
In-house, Claude Code, Codex, or bought: we measure and optimize them.
Cutting review load
Remove human review where it’s safe, with the evidence to defend it.
In production since 2024
Our agents already carry real workloads.
LogicStar Maintain is our SaaS product powered by our bug-finding and bug-fixing agents: it sources, prioritizes and fixes the bugs that matter for maximum ROI. Two years of operating agents for real is where we learned what they need to be measured on. Our own agents run the same loop in production. What teams say about working with it:
“I have infinite things to do. Something that changes what I plan to do for the next few weeks provides value. And LogicStar consistently surfaces issues that do just that.”
“Your team has done a really great job helping us through the PoC. The agent’s quality right now is much better than when we started, and we’ve got some really good validation now.”
“We don't need to spend all that time on employees and build a new process ourselves. LogicStar catches those bugs before they get to production. That's a big advantage for us.”
Before you ask
The concerns we hear, answered.
We measure agents, not developers. Humans keep the merge button, and nobody’s personal stats end up on a dashboard.
Adoption without the backlash. Evidence from your own SDLC gives teams a reason to adopt instead of a mandate to comply with.
Keep the agents you have. In-house, Claude Code, Codex, or bought: we don’t replace your setup, we measure it and make it better.
Numbers you can defend. Every score traces back to a real PR, fix, or incident in your history. When an agent missed something, reality already wrote it down.
A few hours a week from your team. One counterpart engineer and two short workshops; We take care of the forward-deployed engineers, the tooling, and everything else.
Unusual setup? Expected. The benchmark is built from your repositories and your history, so it fits your monorepo, your stack, and your homegrown agent loop by construction.
Built to pass your security review.
read-only startNDAself-hosted · your keysnever trains on your code
FAQ
Everything else teams ask us.
Collected from our customer conversations, answered once for everyone.
What do you need from us to start? +
Read-only access to the repositories your agent works in. Stage one runs entirely on history you already have: PRs, commits, reviews, and the fixes that followed. Later stages add access only when they need it, and you see exactly what each one adds.
How long until we see something? +
Stage one doesn’t wait for new incidents; it runs on history that already exists, so evidence starts arriving as soon as access does. The engagement as a whole is time-boxed, and the scoping workshop sets the box.
Which agents can you work with? +
Any software engineering agent whose work lands in your repositories: in-house harnesses, Claude Code, Codex, or bought tools. If you don’t run an autonomous coding agent yet, we establish the first one with you.
Does our code or data leave our environment? +
It doesn’t have to. Engagements run in your environment with your keys, under NDA, starting read-only, and we never train on your code. The trust page covers the rest of what your security team will ask.
How is it priced? +
The engagement is time-boxed consulting, scoped and priced up front in the CTO-led scoping workshop. The platform is a separate ongoing subscription you keep for as long as it earns its keep; model usage runs on your keys.
Our codebase and our agents keep changing. Do the numbers stay comparable? +
That’s why the benchmark is replayable. Every prompt tweak, agent change, or model update is re-scored against the same held-out history before it touches production, and the benchmark itself is refreshed as your code evolves. A model update that shifts behavior overnight shows up in the score, not in the bill.
Is the goal to remove humans from the loop? +
Only where the evidence says it’s safe, the regulatory environment allows it and you want it. Human review load comes down step by step, each step backed by the benchmark plus evidence, and humans keep control throughout.
Won’t the model providers build this themselves? +
Measurement only works when it’s independent of the thing measured. We don’t sell you a model, so we have no stake in which one wins: your agents are scored on your outcomes, across whatever models you run, and the numbers stay honest when you switch. Also, the model providers have no incentive to build it: they want to sell you more tokens, not to be independently measured and optimized for fewer tokens or replaced by another model.
What happens if we stop? +
You keep everything: the benchmark, the labels, the prompts, the reports. Exportable by design, so leaving is always cheap. We’d rather earn the renewal than lock the door.
FOUNDING TEAM
What you're betting on.
Trusted by:
Built on research
Proven on real-world systems, we publish the leading benchmarks for AI coding agents. That same expertise drives our internal evaluations, so LogicStar keeps getting better as models evolve.
NeurIPS 2024SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents
ICML 2025Automated Benchmark Generation for Repository-Level Coding Tasks
ICML 2025 SpotlightBaxBench: Can LLMs Generate Correct and Secure Backends?February 2026Are Repository-Level Context Files Helpful for Coding Agents?March 2026CodeTaste: Can LLMs Generate Human-Level Code Refactorings?84%
validating tests generated
LogicStar reproduces every bug with a failing test that proves it's real and validates fixes actually resolve them. State-of-the-art performance on SWT-Bench Verified.
Mündler, Müller, He, Vechev
60%
overestimation of success rate in SWE-Bench Verified
Many AI coding agents overfit to a single benchmark. We automatically create new benchmarks for every use-case and show popular code agents lose up to 60% of performance on an application focused benchmark of 366 diverse codebases.
Vergopoulos, Müller, Vechev
33%
of working AI-generated code is exploitable
Even frontier models produce exploitable backends. Across 392 tasks, one in three working solutions contains SQL injection, path traversal, or code injection vulnerabilities.
Vero, Mündler, Chibotaru, Raychev, Baader, Jovanović, He, Vechev
+20%
cost increase, zero performance gain
Over 60,000 repos include AGENTS.md files to guide AI agents. Our evaluation shows these files reduce success rates by up to 3% while adding 20% to inference costs.
Gloaguen, Mündler, Müller, Raychev, Vechev
63%
of AI refactoring attempts break code
AI agents solve only 22% of multi-file refactoring tasks and introduce breakage in 63% of attempts. CodeTaste measures whether AI restructures code the way a senior engineer would.
Thillen, Mündler, Raychev, Vechev