Watch teams of AI agents think out loud.

Hiring Panel

Six specialist agents deliberate to consensus — then rewrite your application

Drop in a job description and CV. Six personas (headhunter, recruiters, hiring manager, coach, company scout) read in parallel, deliberate to consensus in a LangGraph state machine, then a document studio writes four deliverables concurrently. The pattern — parallel expert reads, moderated deliberation, structured output — is how you automate any multi-perspective review: bids, contracts, designs.

Research Agent

Plans its own web research, judges its own evidence, cites every claim

proves: iterative tool use · self-critique · transparent limitations

Ask a question. The agent plans search queries, fetches and reads real pages, then judges for itself whether the evidence is enough — retrying from a different angle when it isn't. Every claim is cited to a page it actually read, and a per-run limitations note says what it couldn't establish. That's the difference between "an LLM said so" and research you can put in front of a client.

Support Ticket Triage

Automates the confident calls, pauses for a human on the risky one

proves: RAG grounding · confidence routing · human-in-the-loop

A pipeline splits an incoming support ticket, grounds each part in a live policy knowledge base, drafts replies, and routes by retrieval confidence: well-grounded parts auto-resolve, and the weakest part checkpoints the run with a real LangGraph interrupt() until you approve one of three options. This is the compliance-grade pattern for agents that act: autonomy where the evidence is strong, human sign-off where it isn't.

Ask My Docs

Answers from your document — and won't invent what isn't in it

proves: scoped RAG · corrective retrieval · groundedness guardrails

Attach a document and ask it questions. A corrective-RAG loop grades every retrieved passage before the writer sees it, rewrites the query when retrieval misses, then checks the finished answer for groundedness — regenerating anything unbacked. Nothing is retained: sessions self-destruct in 15 minutes and export as a portable file. These guardrails are what make an internal doc-bot safe enough to actually deploy.

Due-Diligence War Room

A deep agent researches any real company — plan, sub-agents, red team, your call

proves: planner todos · parallel isolated sub-agents · virtual-FS memory · red-team loop · interrupt() gate

Name a real company. A lead agent plans into a live todo list and spawns four researcher desks in parallel — isolated contexts, capped web budgets — writing findings into a shared virtual filesystem. A red team attacks the resulting thesis, you pick the deal stance at a real interrupt(), and a completion gate structurally verifies the memo before it ships. The same architecture runs any long-horizon knowledge job: due diligence, RFP responses, compliance reviews.

AI Chess Arena

A living benchmark: frontier AI models play each other at chess, nonstop — with every move verified

proves: constrained action space · verifier self-correction · live model benchmark

Which AI model actually reasons best under strict rules? Eight frontier models play continuous round-robin chess, and every move is validated against the legal set — illegal answers get caught, sent back, and corrected, with each model's error rate on the record. The ELO leaderboard builds from real results, the match history keeps the receipts, and the X-ray shows what every single decision cost in tokens. You can also challenge any model yourself and see how you measure up.