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Intelligent AI Agents

Software that takes the action, not just suggests it

Agents differ from assistants in one respect that matters: they change things in your systems. We build them for the narrow, well-bounded jobs where that is genuinely safe, and we tell you which of your ideas is not one of those yet.

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A senior engineer replies within 24 hours — not a sales rep.

What makes agents harder than they look

A demo that works once is not a system

Agent demos are unusually persuasive because you watch a single successful run. The interesting number is what happens over a thousand runs — how often it loops, picks the wrong tool, hallucinates an argument, or declares success on a task it did not complete. That distribution is invisible until you measure it deliberately.

Errors compound across steps

A step that succeeds nine times in ten sounds fine. Chain six of those and the whole run succeeds barely half the time, and the failures land in the middle where partial work has already been written somewhere. Length of chain is the single biggest predictor of an agent being unreliable in practice.

It was given credentials nobody scoped

The fastest way to make an agent capable is to hand it a broad API key. It is also how a retry loop sends the same email four times or updates the wrong hundred records. Agents need their own identity, least-privilege permissions and rate limits — the same treatment you would give any other automated caller, and rarely what they get.

Nobody can explain what it did or undo it

When an autonomous run produces a wrong outcome, someone will ask what it saw, which tools it called, in what order, and whether it can be reversed. If the answer is a log of raw model output with no structure, the honest response is that you don't know — and that is usually the end of the pilot.

How we build agents responsibly

Agentic systems are the least mature part of applied AI, and pretending otherwise does clients no favours. Our approach is to keep autonomy narrow, make every action reversible or approvable, and expand scope only after the measurements justify it.

Narrow scope, explicit tools

An agent gets a defined task, a small set of tools with typed arguments, and a step budget it cannot exceed. Broad open-ended autonomy is where reliability collapses. Most of the value in real deployments comes from agents doing one bounded job well rather than one general assistant doing everything indifferently.

Reversible by default, approved when not

Actions get classified before the build: safe to perform automatically, safe if reversible with an audit entry, or requiring human approval. Payments, external communications, deletions and anything customer-facing land in the third category by default, and moving something out of it is a decision you make consciously.

Measured over runs, not demos

We build a harness that replays a suite of scenarios — including deliberately awkward ones — and reports task completion, step count, tool-call error rate and cost per run. Every prompt or tool change is scored against it, so "the agent got better" becomes a claim with evidence rather than an impression.

Full execution traces, retained

Each run records the trigger, the plan, every tool call with its arguments and response, the model version and the final state change. Operators get a readable timeline rather than raw logs, which is what makes an incident review possible and what an auditor will ask to see.

What this covers

Pick the piece you need, or bring us the problem and we'll tell you which applies.

Agentic AI Systems

A single agent given a goal, a set of tools and permission to decide the order in which it uses them. It suits tasks where the steps vary case by case — investigating an exception, gathering evidence across systems, resolving a mismatch — and is overkill for anything you could write as a fixed sequence.

Tool definitions with typed arguments, validation and explicit failure responses
Step budgets and loop detection so a confused run stops instead of burning tokens
Approval gates on any action classified as irreversible or customer-facing
Per-run traces showing the plan, every call made, and the resulting state change

Multi-Agent Pipelines

Several specialised agents working a task in sequence or in parallel — one retrieves, one drafts, one checks the draft against policy, one commits the result. Worth doing when specialisation measurably beats one agent with a long prompt; often it does not, and we will tell you when a single agent is the better engineering answer.

Explicit handoff contracts between agents so failures localise to one stage
Reviewer or critic stages that can reject and return work rather than only pass it on
Parallel execution with a defined merge and conflict rule for competing outputs
Per-stage cost and latency accounting, since multi-agent designs multiply both

AI Process Automation

Applying agents to a named business process end to end — procurement approvals, claims intake, employee onboarding, order exception handling. The engineering is only half of it; the other half is mapping the real process, including the informal steps that never made it into any policy document.

Process mapping with the people doing the work, exceptions and workarounds included
Agents operating inside your existing tools via API rather than a parallel system
Human checkpoints placed where accountability genuinely sits, not uniformly
Cycle time, exception rate and intervention frequency reported per process

The stack we build on

Chosen to fit the problem — not because it's what we used last time.

Agent frameworks

Claude Agent SDK
LangGraph
LangChain
Model Context Protocol
OpenAI Agents SDK
CrewAI

Orchestration & state

Temporal
Celery
Redis
PostgreSQL
Apache Kafka
FastAPI

Observability & evaluation

LangSmith
OpenTelemetry
Grafana
MLflow
Sentry

Deployment & access control

Docker
Kubernetes
AWS
Azure
HashiCorp Vault
OAuth 2.0

How we'll work together

Every stage ends with something in your hands — not a status update.

01
Task and blast-radius review
We break the intended job into discrete steps and mark what each one can affect if it goes wrong. Steps that touch money, customers or irreversible records get flagged for approval up front. This session often reveals the task is better split into two narrower agents.
You get: A step map with a risk classification per action, the permission scope each tool requires, and a written list of what will stay under human approval.
02
Tools first, autonomy later
We build and test the individual tools as ordinary, deterministic services with typed inputs and proper error handling before any model orchestrates them. A reliable agent is mostly reliable tools; skipping this makes every later failure ambiguous between the model and the plumbing.
You get: A tested tool layer with documented schemas, plus a sandbox environment where runs execute against copies rather than live records.
03
Scenario harness and shadow running
The agent runs across a scenario suite covering the normal path, the edge cases and adversarial inputs, then runs in shadow mode against real triggers — proposing actions without performing them — while your team reviews what it would have done.
You get: A scored scenario report with completion and error rates, and a shadow-run log your operators have reviewed and signed off.
04
Graduated release
Autonomy is switched on for the lowest-risk action class first, at limited volume, with a kill switch and rate caps in place. Scope widens only on evidence from the traces. If a class of task never earns that confidence, it stays in approval mode and that is a legitimate end state.
You get: Production deployment with per-action controls, an operator console with kill switch, trace retention, and a documented escalation procedure.

Where we've applied this

Logistics

Exception handling on shipments — an agent gathers carrier status, checks the contract terms and drafts the customer update, with dispatch left to a coordinator.

Finance

Reconciliation and onboarding checks where an agent assembles evidence across systems and proposes a disposition, and a regulated decision stays with a named person.

Professional services

Research and drafting pipelines that pull from internal precedent and produce a first version, cited, for a fee earner to correct rather than start from nothing.

Retail

Supplier and catalogue maintenance where an agent detects price or stock discrepancies, opens the correction and holds anything above a threshold for review.

Healthcare

Administrative agents handling referral chasing, scheduling conflicts and prior-authorisation paperwork, kept strictly clear of clinical judgement.

Manufacturing

Maintenance triage that correlates sensor alerts with work order history and parts availability, then drafts the job for a planner to release.

Clients we've built for

Real products, in production, with real users on them.

OrbiSim

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Jim's Group

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evera

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National Industries Company

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Questions buyers ask us

Are AI agents actually ready for production work?

For narrow, bounded tasks with human approval on consequential actions, yes — we build those and they hold up. For open-ended autonomy across your business systems, no, and anyone telling you otherwise is selling ahead of the technology. Reliability degrades sharply as the number of steps grows, so the practical question is not whether agents work but how small you are willing to make the job.

Where do you insist on human oversight?

Anything that moves money, contacts a customer, deletes data, alters a regulated record or cannot be undone. Those stay behind an approval step until the trace evidence justifies changing that, and for some categories it never should. We would rather ship an agent that prepares nine-tenths of the work and asks for a click than one that occasionally acts on its own and occasionally embarrasses you.

How is this different from a chatbot?

A chatbot responds to a person, within a conversation, and mostly reads. An agent is often triggered by an event rather than a human, runs multiple steps on its own, and writes to your systems. The oversight, permission model and audit requirements are entirely different as a result. If what you need is answers rather than actions, our chatbot and retrieval work sits under Data & Automation and will cost you less.

What does an agent project cost to build and to run?

Build cost is driven mostly by the tool layer — every system the agent touches needs a reliable, tested interface, and that is ordinary integration engineering rather than AI work. Running cost is less predictable than a chatbot because a single run makes many model calls; we instrument cost per run during the scenario phase so you see the real economics before autonomy is switched on.

Can agents work inside the systems we already use?

Yes, through their APIs, with their own service identity and least-privilege scopes rather than a borrowed staff account. Where a system offers no API we will say so plainly — screen-driven automation against a legacy interface is brittle and we would rather find another integration point than sell you something that breaks on the next vendor update.

What happens when an agent gets something wrong after launch?

You get a full trace of that run, so the review starts with facts rather than guesswork. Operationally there is a kill switch per action class, rate caps, and alerting on completion rate and error patterns. The failure then becomes a scenario in the regression suite, which is how the same mistake stops recurring. Expect to keep tuning for the first months — agents need more post-launch attention than conventional software, and budgeting for that is part of doing this seriously.

Tell us which process you'd hand over first

Describe the task, the systems it touches and what a mistake would cost. A senior engineer replies within 24 hours with a view on whether it is a good candidate for autonomy, where the approval steps belong, and where the answer is not yet.

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