A team, not a single train of thought
Argo breaks the task down, distributes it to specialized roles, and lets them challenge each other before anything is written. That surfaces weaknesses a single agent would miss.
Agent Argo
Describe your goal in natural language. A CEO AI plans, delegates to a team of specialized agents, and has the result independently verified before it counts as done.
CEO AI teamA team of specialized agents challenges each other before anything is written — not a single chat agent.
Vendor-neutral (BYOK)Your own API keys, freely mixing cloud and local models — no vendor lock-in.
Independent verificationA separate verifier agent — not the code's author — checks every result with tests and review, and automatically triggers a repair run on failure.
Full traceabilityEvery decision, context, model, and cost is logged — traceable even months later.
Telemetry
Measured, not claimed
Argo measures the cost per accepted result — not tokens. These figures come from anonymized opt-in telemetry of real installations: aggregate numbers only, never project content.
Not enough anonymized usage data yet to display figures — they appear once the minimum sample size is reached.
–avg. minutes saved per accepted run
–estimated developer hours saved
–estimated net savings (€)
–of runs are accepted
–rollback rate
SWE benchmarks
Estimated figures — real test runs to follow
The figures are deliberately estimated on the pessimistic side — we'd rather undersell than promise something Argo doesn't (yet) reliably deliver. Each percentage is the resolve rate: the share of benchmark tasks solved completely correctly — no time comparison to other models and no partial credit for half-right solutions. These are reasoned estimates, not measured results: derived from the underlying models, the orchestration, and Argo's verification layer — not from benchmark runs that were actually executed. Real test runs are on hold for now due to the potentially high API cost (many model calls per task) and will follow later.
Ultracode is still under active development — these figures are preliminary estimates.
Estimates, not measurements — real benchmark runs are on hold for cost reasons and will be added once available.
Blog
Blog: follow development live
What we're building right now, which new techniques we're deploying, and what changed in the latest release.
Positioning
Not a chatbot. An artificial software team
A single agent quickly delivers a plausible solution. But plausibility isn't reliability. Argo treats planning, implementation, review, and sign-off as separate responsibilities — like a good team.
Autonomy with guardrails
Changes start out as a patch in an isolated environment and are only applied after review. You decide what Argo may do automatically — critical actions always stay specially protected.
Every decision leaves a trail
Every run logs what happened: which context, which model, which costs, which checks. Even months later, you can trace why a decision was made.
How it works
From requirement to verified result
You describe a goal in natural language. Argo turns it into a plan, orchestrates the right agents, and delivers a result that doesn't just look finished — it was verified.
- A scheduler and replanner decide which agents and models are actually needed
- Independent verifiers check results instead of just summarizing them
- Context packages deliver the relevant parts of large codebases, precisely targeted
- Git mode: commits, branches, and PR descriptions drawn from the work performed
CEO AI
Understand the task, build a plan and task graph
Understand the task, build a plan and task graph
Agent team
Architecture, development, QA, docs — with structured deliberation
Architecture, development, QA, docs — with structured deliberation
Verification
Tests, reviews, independent verifiers, repair runs
Tests, reviews, independent verifiers, repair runs
Result
Verified patches, rationale, and a complete audit trail
Verified patches, rationale, and a complete audit trail
Economics
The right metric: cost per accepted result
Argo often burns more tokens per task than a single chat — and still comes out cheaper. Routine work runs on cheap or local models; expensive frontier models are used only where their added value justifies the added cost.
- Intelligent model routing across price tiers, with explainable model selection
- Soft and hard budget limits, simulated before a run starts
- Fewer retries, less rework, less review time — human labor is the far more expensive resource
Routine
Context lookup, docs, simple checks → cheap or local models
Context lookup, docs, simple checks → cheap or local models
Demanding
Architecture, complex implementation, root-cause analysis → strong models
Architecture, complex implementation, root-cause analysis → strong models
Safeguards
Verification and escalation only where they bring measurable value
Verification and escalation only where they bring measurable value
Next step
Better results with less oversight
Less rework, less review time, more trust in the result — that's the difference between an agent and a team.