Systems that choose their own next step — tool-using agents, orchestrated workflows and automation that spans your existing systems — designed so that what they are permitted to do, and what happens when they are wrong, are decided up front.
The hard part of an agent is almost never the model. It is the boundary: what it may do unsupervised, how a half-finished multi-step action is recovered, and how you stop it. Most production systems marketed as agents are workflows with a model in them — and that is usually the correct design, chosen deliberately rather than by default.
What we build
Agents whose permissions are scoped by least privilege, whose irreversible actions require a human, and whose every step is logged in a trajectory you can replay. Reversibility tiers decided at design time, not after the first incident.
Routers, orchestrator-worker patterns and parallel fan-out with a reducer, chosen by what the problem needs rather than by what is fashionable. We start with the simplest loop that works and escalate only on evidence.
The quality of an agent is mostly the quality of its tools. Interfaces designed for a model rather than a human — naming, granularity, idempotency, error messages written as instructions — exposed over the Model Context Protocol so they are reusable across systems.
Replay harnesses with recorded tool responses, outcome and trajectory scoring, step and cost budgets asserted in CI. An agent that reaches the right answer after forty expensive steps has still failed, and your harness should say so.
Sandboxed execution, durable and resumable runs, step-level idempotency, circuit breakers and runaway-cost protection, rate limiting against downstream systems, and a documented way to stop one mid-flight.
The hard parts
A vendor who cannot tell you where their own discipline is difficult has either not done it or is not going to tell you the truth about your project either.
Every increment of independence costs you the ability to say in advance what the system will do. That is sometimes worth paying and frequently is not, and the decision should be explicit rather than inherited from a demo.
Several agents talking to each other have the same communication-overhead problem several teams do, without the judgement to route around it. More agents is usually a worse answer than one well-designed loop with better tools.
An agent rarely fails loudly. It produces something that looks right, in the right format, at the right time. Detection has to be engineered — budgets, assertions, evaluation — because it will not announce itself.
Stack
The full stackThis is what we reach for when the decision is ours. When you already have an estate, we work in it — the stack is part of the engagement, not a precondition of it.
Roles we staff
The reasoning, published
Every capability has a matching domain in the knowledge library. Read it before you talk to us — it is the fastest way to tell whether we know what we are doing.
Single loop, prompt chain, router, orchestrator-worker, parallel fan-out and multi-agent. What each pattern is genuinely for, the coordination cost that multi-agent quietly reintroduces, and a selection table you can argue from.
Practitioner · 9 minContext is a scarce, expensive, actively managed resource rather than a place to put everything. Working versus persistent memory, what compaction loses, sub-agent isolation, and why long-running agents degrade.
Advanced · 10 minThe design question for an agent is not which model it uses but what it is permitted to do. Authority boundaries, idempotency, blast-radius limits, prompt injection as a live threat, and audit trails that hold up.
Advanced · 10 minThe unit of evaluation is a trajectory, not a response. Outcome versus trajectory assertions, step and cost budgets as first-class checks, replay harnesses, and why an agent that succeeds expensively has still failed.
Practitioner · 9 minThe operational surface of a system that acts on its own. Sandboxing, durable execution, circuit breakers and runaway cost, rate limiting, trajectory observability, on-call, and how you stop one mid-run.
Advanced · 10 minThe quality of an agent is mostly the quality of its tools. How to design interfaces for a model rather than a human, why error messages are instructions, and what MCP standardises without solving.
Practitioner · 10 minHow to buy it
Capability answers who works on it. The engagement model answers what we are accountable for, how it is priced and how it ends.
Other capabilities
Probabilistic systems
LLM applications, retrieval systems, agents and applied ML built to a standard you can put in front of a regulator — with evaluation harnesses, cost controls and failure behaviour designed in rather than discovered in production.
Irreversible deployment
Protocol and application development where a deployment cannot be rolled back and a defect is a withdrawal. Contract engineering, security review, upgradeability design and the on-chain boundary decision.
Gated releases
iOS, Android and cross-platform applications delivered continuously despite a third party sitting between your merge and your users — staged rollouts, feature flags, release trains and a native-versus-cross-platform decision made on economics.
The paved road
The substrate the other three stand on: cloud infrastructure, deployment pipelines, observability, data platforms and the internal paved road that determines how fast every team above it can move.