// 01 · Service
AI agent development
Tool-using agents that act on your systems — calling your APIs, running workflows, deciding what to do next — with guardrails, retries, and traces so they are safe to run unattended in production.
- LangGraph
- OpenAI
- Anthropic
- River
// The problem
Why this is hard
A demo agent that works in a notebook is a long way from one you can trust with real customer actions. Naive agents loop, hallucinate tool calls, and fail silently — so the hard part isn't the first response, it's making the agent safe to run unattended: bounded, observable, and recoverable when a step goes wrong.
// What we build
What you get
Agent & tool architecture
The graph of steps, tools, and decision points — scoped so the agent can only take actions you've sanctioned.
Guardrails & fallbacks
Input/output validation, retries, and safe fallbacks so a bad tool call degrades gracefully instead of cascading.
Traces & observability
Every run is traceable end-to-end, so a wrong decision is debuggable rather than a mystery.
Human-in-the-loop review
The ambiguous cases route to a person with full context, so automation stays bounded to what it does reliably.
// How it fits together
The system we build
- Requestuser / event
- Plangraph of steps
- Scoped toolssanctioned actions
- Guardrailsvalidate + retry
- Human reviewambiguous only
- Acttraced end-to-end
// Deliverables
- Agent & tool architecture
- Guardrails and fallbacks
- Agent run traces
- Human-in-the-loop review
// How we work
From prototype to production, in four moves.
Discovery
We map the problem, the data, and the eval that defines "done".
Prototype
A working slice in weeks — real model, real data, measured.
Production
Hardened, observable, evaluable. Shipped where users live.
Scale
Cost, latency and reliability tuned as load and scope grow.
// Typical engagement
What it takes to work together
Architecture Sprint
$4–8kfixed · 1–2 wks
1–2 weeks
De-risk before you build — architecture, a plan, and a working proof-of-concept.
Build
from$20kfixed scope or pod
typically 1–3 months
Ship the product end-to-end, in your repo and conventions.
Run & Scale
from$4k/ month
ongoing
Operate and improve after launch — SLA, monitoring, and iteration.
Indicative ranges — the final price and timeline depend on your project's scope, complexity, and integrations. A paid Architecture Sprint pins them down.
// Proof
Shipped in production
E-commerce — Support agent
tickets auto-resolved
of incoming tickets, end-to-end
first-response time
vs. the pre-agent baseline
// FAQ
Common questions
// Related
RAG & Knowledge Systems
Retrieval grounded in your own data, with an eval harness that proves the answers are faithful to the source — not plausible-sounding guesses.
LLM Integration & Evals
Models wired into your product behind an interface you control, provider-agnostic by design. Every prompt and model change is gated on evals — measurable quality, not vibes.
Product Engineering
Full-stack, AI-native products from first prototype to a system users trust — designed, built, and shipped in your repo and your conventions.
MLOps & Infra
The queues, workers, observability, and cost controls that keep models fast, reliable, and on budget once real traffic arrives.
AI Strategy & Audit
A clear-eyed read on where AI pays off and where it does not — an architecture review, an honest risk and feasibility assessment, and a prioritised roadmap before you commit budget.
// Industries
Where teams put this to work:
// Let's build
Ready to build with ai agents & automation?
Tell us where you are. We reply within a day with a concrete next step.