We design, build, deploy and debug autonomous AI systems — MCP servers, LangGraph state machines and deep multi-agent architectures — then instrument them so you always know exactly why an agent did what it did.
Fluent across the agent stack
Agents shipped to production
Median tool-call success after tuning
Average inference spend reduction
Typical time to first agent in prod
Most teams can prototype an agent in a weekend. Getting one to run reliably, cheaply and observably for eighteen months is an entirely different engineering problem. That gap is our whole practice.
Architecture first. We model your problem as an explicit state machine — nodes, edges, interrupts, checkpoints — before a single prompt is written. Deterministic control flow around a non-deterministic core is what makes agents debuggable later.
Agents are long-running, bursty, stateful and expensive — a genuinely hostile workload for ordinary request/response infra. We ship durable execution, queue-backed workers, streaming transports and blue/green rollouts gated on evaluation suites, not vibes.
Our specialty. We take traces from agents nobody can explain any more and find the actual failure — a malformed tool schema, a poisoned memory write, a retry storm, a silent context truncation — then prove the fix with a replayable regression suite.
Models deprecate, APIs drift, prompts rot. We run the on-call rota, keep the eval suites honest, migrate you between model generations without regressions, and publish a monthly reliability and cost report your CFO can actually read.
OpenTelemetry spans on every node, tool call and token. Cost, latency and quality attributed per run, per customer, per prompt version — so regressions surface in a dashboard instead of a support ticket.
We are deliberately polyglot. The right runtime for a latency-bound voice agent is not the right runtime for a nightly batch of ten thousand document extractions. Drag the constellation to explore.
LangGraph
Durable, checkpointed state machines for agents that need to pause, branch, resume and be inspected.
We map the workflow into an explicit graph: what the agent is allowed to decide, what must stay deterministic, where a human approves. Ninety percent of agent failures are scoping failures wearing a prompt costume.
Every capability becomes a typed, versioned, individually testable tool — usually an MCP server so the same integration works across Claude, your IDE and your production runtime. Strict schemas, idempotency keys, and errors the model can actually act on.
A golden dataset from your real traffic, LLM-as-judge scoring calibrated against human labels, plus adversarial cases for injection and loop behaviour. No prompt change merges without a green run.
Checkpointed execution so a pod eviction at minute forty doesn't lose the run. Streaming to the client, budget ceilings per invocation, and a kill switch that actually works mid-flight.
Traces flow into OpenTelemetry. Every anomalous run is replayable locally against the exact tool responses it saw. Weekly, the worst traces become new eval cases — the loop closes and quality compounds.
Bring us a trace and a symptom. These are the six failure classes behind the overwhelming majority of production incidents we're called into — select one to see how it presents.
Our fix: a hard step ceiling with a progress heuristic — if no new information enters the scratchpad across two cycles, the graph routes to a summarise-and-escalate node instead of re-planning.
An agent that only speaks to your own code is a dead end. The industry settled on three standards — one for tools, one for other agents, one for people — and we build against all of them, so your system stays pluggable as the ecosystem keeps moving underneath it.
Model Context Protocol turns your internal systems into typed, scoped tools any model client can call. We ship servers over stdio and streamable HTTP, with OAuth-scoped access, contract-tested schemas and gateways in front of the whole estate.
Agent2Agent lets your agents discover and delegate to agents you do not own or control. We publish signed Agent Cards, negotiate capabilities, and put timeouts, retries and settlement logic around every cross-vendor handoff — because a peer agent will fail.
An agent nobody can watch is an agent nobody trusts. AG-UI streams tokens, tool calls and state deltas into your front end over a standard event channel, so you get live progress, shared state and human approval gates without a bespoke websocket protocol.
Representative engagements. Names withheld under NDA; numbers verified against the client's own telemetry ninety days post-launch.
A supervisor graph triages chargeback claims across six internal systems, drafts the rebuttal packet and escalates anything below a confidence floor to a human queue.
73%
Cases fully automated
4.1×
Faster resolution
Forty-one fragmented service integrations consolidated behind one governed MCP gateway with SSO, per-team quotas and full audit trails.
41→1
Integrations to maintain
9 wks
To full rollout
Inherited a prior-auth agent quietly burning $190k a month. Trace forensics found a recursive retrieval loop re-embedding its own output. Rebuilt with caching and step caps.
−88%
Monthly spend
11 d
Diagnosis to fix
No six-month discovery phase. We aim to have something running against your real data inside the first fortnight, because that is when you learn what the agent actually needs to be.
Book a scoping callTwo working sessions with your engineers. We leave with a graph diagram, a tool inventory, a failure budget and an honest answer on whether you need an agent at all — sometimes the correct recommendation is a workflow.
One complete path end to end, on your real data, in your VPC. Instrumented from day one so we're measuring rather than guessing.
Golden datasets, adversarial suites, cost ceilings, fallbacks and the human-escalation paths. This is where an impressive demo becomes a dependable system.
Shadow mode, then a percentage ramp with automatic rollback on eval regression. Your team runs the deploy; we're on the call.
Full documentation, runbooks and pairing until your team owns it — or we stay on as the agent reliability function. Both are fine; we're not trying to become load-bearing.
The things engineering leaders ask us on the first call.
Send us the symptom, the trace, or just the ambition. You'll get a technical reply from an engineer who has debugged this exact class of problem before — not a sales sequence.
Typical first response within one business day · Remote-first, GMT−5 to GMT+5:30 coverage