Multi-agent architecture, orchestration graphs, tool calling, state, evaluation and guardrails. This is the engineering layer most AI projects skip — and the reason they stall somewhere between the pilot and production.
Almost every stalled AI project looks the same from the inside. The prototype was impressive. Then it met real inputs, and the failures were not language failures — they were systems failures. A run died halfway with no way to resume. A retry created a duplicate record. Nobody could explain why one customer got a good answer and the next got nonsense, because there was no trace. A model upgrade shipped and quality quietly dropped, because nothing measured it.
That is architecture work, and it is the same discipline as any distributed system: explicit control flow, durable state, bounded retries, idempotent side effects, observability, and tests. The model is one component inside it — an unusually capable, unusually unpredictable component that needs to be contained accordingly.
We design that containment. Where the model decides and where code decides. What each agent is allowed to touch. What happens on the unhappy path. How you will know, six months from now, whether the thing is still working.
We decide the shape before writing prompts. A supervisor that routes to specialists suits work with clean task boundaries. A planner-executor pair suits open-ended requests that need decomposition first. A simple chain beats both when the steps are known. The wrong topology cannot be fixed with better prompting, and the most common mistake is reaching for multiple agents when one would do.
Tool design is prompt engineering that actually compounds. A tool with a vague name and a fuzzy description gets called at the wrong moment; a tool with a tight contract and an unambiguous description gets called correctly nearly every time. We keep parameter surfaces small, validate arguments against schema before execution, and return errors the model can act on rather than opaque stack traces.
Where the tool surface should be reusable across agents, we expose it through Model Context Protocol servers so the same capability is available to every system that needs it, under its own scoped credentials.
State in a variable is state you lose. We persist run state at checkpoints so a workflow survives a deploy, an outage or a customer walking away mid-conversation and coming back an hour later. Memory is split deliberately: short-term working context for the current run, durable memory on the contact or account for facts that should still be true next month.
And for every branch that must not improvise, there is a deterministic fallback. If the model is unavailable, ambiguous or over budget, the system takes a known path — a template, a rule, a human — rather than degrading silently.
An eval harness turns opinion into evidence. We build one from your own history — real inputs, expected outcomes, known edge cases — and run it on every prompt, model or tool change. It is what makes a model upgrade a five-minute decision instead of a quarter-long anxiety.
Guardrails sit alongside: refusal boundaries, approval gates on irreversible actions, and strict separation between instructions and untrusted content so a prompt-injection attempt buried in an email cannot redirect an agent with write access. Routing decides which model handles which node, measured against cost and eval score rather than assumption.
Every other capability in the automation layer sits on top of this one. Chat and voice agents need the orchestration to know when to escalate. Retrieval needs somewhere disciplined to plug in. Integrations need idempotency and retry policy or they will quietly corrupt your records. Getting this right once means the fifth workflow costs a fraction of the first.
It also reaches outward. An agent is only as good as the systems it plugs into — when your stack cannot support the automation, we build the stack. And when the graph decides a case needs judgment, trained people handle the exceptions and escalations as part of the same system, not as a separate department with its own inbox.
A supervisor topology, reduced to the parts that decide whether it survives a bad week.
We separate what genuinely needs language understanding from what is a rule someone never wrote down. That single pass usually shrinks the AI surface area and makes the whole system more predictable.
30-min call · roadmap deliveredControl flow, tool contracts and the eval suite are built in parallel, not sequentially. Nothing reaches production without a case set it has to pass and a trace you can replay.
Typically 3–6 weeksEscalation rate, tool-call accuracy, latency and cost per run stay on a dashboard. New models get tested against the suite and swapped in when they win on both quality and price.
Ongoing partnershipWhen one agent needs contradictory instructions or more tools than it can reason about reliably. A single agent handling intake, research, drafting and approval will do all four adequately and none well. Splitting them gives each a short instruction set, a narrow toolset and its own evaluation. If one agent with four tools works, we keep it — complexity is a cost.
LangGraph most often, because explicit graphs with durable state fit production work better than free-form agent loops. But the framework is a smaller decision than people expect. What matters is that control flow is inspectable, state survives a restart, and every step can be replayed. We have built the same architecture directly against provider SDKs where a dependency was not welcome.
Hard limits plus visibility. Every graph has maximum step counts, per-run token budgets and timeouts, and a run that hits one exits to a defined terminal state rather than spinning. Cost is tracked per run and per workflow with alerting on anomalies, so a change that triples spend surfaces the same day rather than on the invoice.
It is a fixed set of real cases with expected outcomes, run automatically whenever a prompt, model or tool changes. Without one, every change is a guess and every model upgrade is a risk. With one, you see exactly which cases improved and which regressed before the change reaches a customer. We build it before launch, not after the first incident.
Frequently. Typical inheritance work is a prototype that works in demos and fails in production — no state persistence, no evals, no tracing, secrets in prompts, unlimited loops. We start by instrumenting it so failures become visible, build an eval suite from real cases, then refactor the control flow. Rewriting from scratch is a last resort, not an opening move.
By measuring, per step. Classification and routing rarely need a frontier model; multi-step reasoning and drafting usually do. We route each node to the cheapest model that passes its evals, and keep routing configurable so a new model release can be tested against the suite and swapped in without touching application code.
Bring us the prototype that works in the demo and breaks in the wild. Thirty minutes is usually enough to name the architectural gap and what it takes to close it.
Roadmap delivered · whether or not you build with us