Agents that complete the work, not chatbots that describe it
An agent earns its place when it reads your systems, decides what to do, does it, and leaves a trace you can audit. We build on LangGraph and CrewAI with human approval gates on anything consequential, and self-hosted n8n where a deterministic workflow is the better answer.
Most agent demos collapse the moment a tool call fails
A demo agent runs one happy path in front of an audience. A production agent runs thousands of times a week against APIs that rate-limit, records that contradict each other and edge cases nobody wrote down. The difference is not the framework. It is the engineering around the loop.
We design agents as state machines with explicit transitions, so you can see where a run is, why it took a branch and what it did to your systems. Anything that spends money, contacts a customer or changes a record passes through an approval gate until you have the confidence to remove it.
We are also willing to tell you when an agent is overkill. Plenty of workflows that get pitched as agentic are a scheduled job with three conditionals, and they run more cheaply and reliably that way.
Why these projects fail
The failure patterns are consistent across the agent projects we are asked to take over.
How we take an agent to production
Each stage narrows the blast radius before the next one widens the autonomy.
Workflow mapping
We sit with the people doing the work and document the real process, including the exceptions they handle by instinct. That document decides which steps an agent should own and which stay human.
Tool and data contracts
Every system the agent touches gets a typed interface with validation, timeouts and explicit error states. The agent never sees a raw failure it can misread as data.
Graph design
The workflow becomes an explicit LangGraph state machine with defined nodes, transitions, retry policy and step limits. Deterministic steps stay deterministic.
Approval gates
Actions with consequences route to a human queue with the reasoning attached. Gates are removed one at a time as the confidence data supports it.
Trace observability
Every run is logged end to end in LangSmith: inputs, tool calls, intermediate reasoning, outputs and cost. Failures are searchable rather than anecdotal.
Shadow running
The agent runs alongside the existing process without acting, and we compare its decisions to the human ones until the gap is small enough to switch over.
Live rollout and tuning
Staged rollout by team or queue, with alerting on failure rate, latency and cost per run, plus a monthly review of where the agent still hands back.
An agent run, drawn as the state machine it should be
Nothing here is implicit. Every transition, retry path, approval gate and cost ceiling is a design decision made before the agent touches a live system.
What lands in your repository
An agent you can operate, extend and switch off without calling us.
Tools we build this on
Chosen per project against your data, latency budget and compliance requirements, not out of habit.
Agent Frameworks
Workflow Engines
Models
Observability
Agent systems running in production today
Numbers from live production systems, not projections.
An autonomous revenue layer running 900+ triggers a day
Lead qualification, CRM enrichment, personalised outreach and deal health scoring on n8n and LangGraph, generating $340K of pipeline in six months with 48% less manual sales operations work.
E-Commerce · USASupport across seven channels with flat headcount
Claude Sonnet handles incoming queries with knowledge-grounded reasoning, resolving 61% without escalation and cutting average response time 44%, with sentiment-based escalation to humans.
DevOps · USAMulti-agent platform inside an existing DevOps toolchain
Pull request review, pipeline failure diagnosis, validated patch generation and structured incident summaries, cutting debugging and incident time 38%.
Agent questions we answer on most first calls
What clients ask before starting a project like this.
What is the difference between an agent and a workflow automation?
A workflow follows a fixed path you defined. An agent decides the path at run time based on what it finds. Workflows are cheaper, faster and more predictable, so we use them wherever the process is stable and reach for an agent only where genuine judgement is needed at each step.
How do you stop an agent from doing something damaging?
Least-privilege credentials, typed tool contracts with validation, explicit step limits and human approval gates on any action that spends money, contacts a customer or writes to a system of record. Gates come off one at a time once the trace data supports it.
Can agents work with our internal systems and not just SaaS tools?
Yes. Most of our agent work connects to internal databases, private APIs and legacy systems through a service layer we build. Where you need everything on your own infrastructure, we self-host n8n and the model layer inside your environment.
What does it cost to run an agent in production?
Cost per run depends on model choice, context size and how many steps the graph takes. We instrument cost per run from the first sprint and tune it, usually by routing simple steps to smaller models and caching what does not need to be recomputed.
How long does an agent project take?
A single-workflow agent typically takes 3 to 5 weeks including shadow running. Multi-agent systems spanning several departments run 8 to 16 weeks, with a working demo from sprint two onward.
Which of your workflows still runs on copy and paste?
Describe one process your team repeats every week. We will tell you whether it needs an agent, a deterministic workflow or neither, and what the build would involve.