What We Build

Agentic AI & Multi-Agent Workflow Automation

Agentic AI describes systems in which a language model does not merely answer a question but plans a sequence of steps, calls tools and business systems to carry them out, checks the result of each step, and adapts the plan when something fails.

A retrieval system tells someone what the contract says. An agentic system opens the renewal, checks it against the procurement policy, drafts the notice, files it for approval and updates the ERP. The difference is between answering and acting, and it is where the second wave of enterprise AI value sits.

It is also where the risk moves. A wrong answer is embarrassing; a wrong action has consequences in a system of record. The engineering discipline that matters in agentic work is therefore almost entirely about constraint: what an agent is allowed to touch, what it must ask permission for, what it does when a step fails, and what record it leaves behind.

We build agents that are narrow, permissioned and observable by default. An agent that can do three things reliably and refuses the fourth is worth considerably more than one that will attempt anything.

What we build

Most real workflows are not a single agent. They are a small set of specialised agents with a clear division of responsibility and an orchestrator that routes between them, because a narrowly scoped agent is easier to evaluate, to permission and to debug.

Workflow decomposition — mapping the existing process before automating any of it, including the exceptions people handle informally
Tool and system integration against your ERP, CRM, ticketing, email and internal APIs
Scoped, least-privilege credentials per agent, so the blast radius of a mistake is bounded by design
Human approval gates on any step that spends money, commits the organisation, or writes to a system of record
Deterministic fallbacks and retry logic, so a transient failure does not abandon a half-finished workflow
Complete action logging — what was decided, on what evidence, which tool was called, what came back

Where the approval gates go

Deciding which steps run unattended and which require a person is a business decision, made explicitly during the architecture phase and revisited as confidence builds. A typical first deployment automates gathering, checking and drafting, and stops for a human before anything is submitted or sent.

As the action log accumulates evidence that a given step is reliable, gates can be moved rather than removed wholesale. That progression is deliberate: it is how an organisation earns confidence in an automated process instead of asserting it on day one.

Making it observable

An agentic system that cannot be inspected cannot be operated. Every run produces a trace showing the plan the agent formed, the evidence it retrieved, each tool call and its result, and the point at which a human intervened. When something goes wrong — and it will — that trace is the difference between a fix and a rewrite.

The same traces are what make the system auditable to a compliance function, which in a regulated environment is usually a precondition for deploying it at all.

02Where It Applies

Typical applications of Agentic AI & Workflows.

Procurement workflows from requisition through supplier check to purchase order

Compliance monitoring that detects a regulatory change and opens the internal review

Invoice exception handling and three-way match resolution

Onboarding and KYC evidence collection, verification and escalation

Order-to-cash chasing, reconciliation and dispute triage

Conversational ERP — natural-language commands that execute real transactions

Incident triage that gathers context, proposes a cause and routes to the right team

03Questions

Agentic AI & Workflows, answered.

The questions that come up on almost every first call about this capability.

What stops an agent from doing something it should not?

Three layers, and none of them is the prompt. First, credentials: each agent gets least-privilege access to only the systems and operations its job requires, so most damaging actions are not available to it at all. Second, approval gates on any step with real-world consequence. Third, full logging, so anything unexpected is visible immediately rather than discovered later. Prompt instructions are a useful fourth layer, but designing a system that depends on them is a mistake.

How is this different from the RPA we already run?

Traditional RPA follows a fixed script against fixed screen positions and breaks the moment anything changes. An agentic system works from intent: it reads the situation, decides which step is appropriate, and adapts when a document arrives in an unexpected format or a system returns something unusual. Practically, this means it handles the long tail of exceptions that RPA hands back to a person — and that long tail is usually where the remaining cost is. They coexist well; agents often call existing RPA as one of their tools.

What happens when an agent gets stuck?

It stops and escalates, with the full trace of what it was attempting and where it failed. The design principle is that an agent unable to complete a step reliably should hand it to a person with context, not improvise. Silent failure and creative workarounds are the two behaviours we specifically engineer against.

Do we need to change our existing systems?

Usually not. Agents work through the interfaces your systems already expose — APIs where they exist, and other integration paths where they do not. The point of an agentic layer is to sit above systems that were never designed to talk to each other, which is precisely the situation that makes the manual process expensive today.

How do you measure whether it is working?

Against the process metric that justified building it — cycle time, exception rate, cost per transaction, share of cases completed without human touch — established as a baseline during discovery. Agentic systems are particularly prone to looking impressive in a demo and underperforming in production, so the business case is agreed before any code is written and the system is measured against it afterwards.

04Sectors