Autonomous and multi-agent systems that take real work off your team: ops, support, research, sales, internal tools.
Agents are software that decides. We build agentic systems with LangGraph, OpenAI Agents SDK, or Claude with MCP that do real work: reading inboxes, drafting outreach, triaging tickets, running research loops, or orchestrating multi-step backend workflows. We start with one job-to-be-done, wire in real tools, and bake in evals so the agent gets measurably better over time.
An AI agent is software that decides. It reads, plans, calls tools, checks its own output, and ships work to a human inbox at the end. Agents shine when the task has multiple steps, fuzzy inputs, and a clear definition of done.
We build agents with LangGraph (state machines for graph-based control), OpenAI Agents SDK (single-agent tool-use loops), or Anthropic Claude with the Model Context Protocol (MCP) when clean tool typing matters. For multi-agent setups we use planner / worker / critic topologies in LangGraph, or OpenClaw-style Claude coworker orchestration when several Claude sessions need to share context on the same task.
Every agent we ship has: evals, tracing, idempotent tools, per-task rate budgets, and a human-in-the-loop step for anything that costs money or touches a real customer.
One agent, one clear job: research, outreach, triage, QA. Built with LangGraph or OpenAI Agents SDK, tool-using, evaluated, deployable on day one.
Planner / worker / critic architectures using LangGraph state machines, CrewAI, or AutoGen. We design supervisor / sub-agent topologies that converge instead of looping forever.
Anthropic Claude with the Model Context Protocol (MCP) for clean tool access: CRMs, ticketing, Slack, calendars, internal APIs. Includes OpenClaw-style multi-agent orchestration when several Claude sessions need to cowork on the same task.
Trace every step with LangSmith, Langfuse, or OpenTelemetry. Score every run, ship improvements with confidence.
Reads tickets, drafts SOP responses, escalates the weird ones. Replaces the bottom 60% of ops work without replacing the team.
Finds companies, reads filings, scores fit, drafts personalised outreach. Builds a queue your sales team actually wants to work.
Planner generates questions, workers fetch and read, critic scores and rewrites. Weekly memo, partner-ready.
Slack-first agent that queries your warehouse, runs the SQL, and returns the answer. No more "can someone pull this number?"
Reads PRs, design docs, customer emails, and flags risks. Frees senior reviewers for the calls that matter.
Calendar + CRM + email + Slack, wired through MCP. The assistant that actually does the thing instead of suggesting it.
Our agents do specific work end to end. They are measured against the human task they replace, not against benchmark scores.
Every agent ships with a test harness. Quality is a number on a dashboard, not a vibe.
Per-task rate budgets, prompt caching, smart routing between cheap and expensive models. Production agents do not blow up your token bill.
Observability via LangSmith or Langfuse. Retry-safe tools. Idempotent side-effects. Built so a 2am page does not become a 4am page.
Want the full picture across every service? See the complete stack.
Built an AI ops co-pilot that drafts SOP responses, triages tickets, and writes weekly performance digests for a 50-person operations team, cutting average ticket time by 62%.
A planner-worker-critic agent system that scouts companies, reads filings, and synthesizes weekly memos, replacing 2 analysts of grunt research with one clean inbox.
Depends on the agent. LangGraph wins when you need explicit state, branching, retries, or multi-agent coordination — basically anything that benefits from a state machine. OpenAI Agents SDK wins for single-agent tool-use loops where the model decides what to do next and you mostly care about velocity. For Anthropic-native projects we use Claude with MCP for cleanly typed tool access.
MCP (Model Context Protocol) is Anthropic's open standard for giving Claude clean access to tools, data sources, and prompts. We build MCP servers for client systems so Claude (and any other MCP-aware model) can use them safely. Standard MCP transport with stdio or HTTP+SSE, fully observable.
Yes. Claude Code is part of how we ship faster internally. For client projects we can also set up Claude coworkers inside your org so your team gets the same leverage after we hand off. OpenClaw-style multi-Claude orchestration is how we coordinate several Claude sessions on the same long-running task.
Three layers. (1) Per-task token + cost budgets enforced at the orchestrator. (2) Tool-call allowlists and dry-run modes for anything that costs money or touches customers. (3) Eval harness on a labelled dataset, run on every prompt change, gating deploys.
Yes. Self-hosted Llama or Mistral via vLLM on your AWS or GCP, with LangGraph or your preferred framework on top. Used for clients in regulated industries.
Tell us about the workflow you want to fix, the bottleneck you want gone, or the product you want shipped. We come back within 24 hours, usually with a few sharper questions.
