Agentic AI & Automation
An AI agent is a system that completes multi-step work on its own: it reads context, decides what to do next, calls your tools and APIs, and produces a result rather than a suggestion. We design, build and deploy agents for defined operational workflows — support triage, document processing, data reconciliation, research and reporting — with explicit boundaries, human approval gates, and measurement of how often they are right.
The problem
The work between the systems
The queue only moves when someone opens it
Operational work runs on shift hours. A request arriving at five on Friday sits until Monday morning while the response clock keeps running, and the backlog that builds over a long weekend is worked through in the order it is found rather than the order that matters.
Six tabs and a decision that takes ten seconds
The person handling the case reads the request, opens the CRM, checks an order in a second system, cross-references a spreadsheet one team maintains privately, then types the outcome back into the first system. The judgement is quick. Everything around the judgement is not, and it is most of the job.
The exceptions are the job, and they are undocumented
The written procedure covers the standard case. The variants — the account with the unusual contract, the request that arrives in the wrong format, the customer whose situation the policy never anticipated — get resolved by whoever has been there longest, differently each time, with no record of the reasoning.
What a real fix takes
The hard part is not getting an agent to act. It is deciding what it is not allowed to do, and being able to prove it. Narrow scope, permissions granted per action rather than wholesale, defined behaviour when a tool is down or the input is malformed, and a test set built from cases your team has already resolved — none of that is what gets demonstrated in a sales meeting, and all of it is what decides whether the agent is still switched on in month six.
Outcomes
What changes once it is running
Work that used to queue gets done continuously
Agents do not batch. A ticket classified and routed the moment it arrives, an invoice reconciled at 2am, a report compiled before anyone asks for it. The gain is often cycle time rather than headcount.
Your people move up a level
The realistic pattern is not replacement, it is the agent handling the routine majority while humans take exceptions and judgement calls. Teams typically report the job getting more interesting, and that matters for whether the deployment survives contact with the people using it.
Consistency you can audit
A well-built agent applies the same policy every time and logs why it did what it did. That trail is frequently more valuable than the automation itself, particularly in regulated environments.
Honest limits, stated up front
Agents are strong where work is repetitive, rule-adjacent and text-heavy. They are weak where the process is undocumented, exceptions outnumber the norm, or a wrong answer is expensive and hard to detect. We tell you which category you are in during scoping, not after invoicing.
What we build
The parts that decide it
Scoped workflow agents
An agent with a defined job, a defined set of tools, and defined stopping conditions. Not a general assistant. Narrow scope is the single strongest predictor of whether an agent works in production.
Tool and system integration
The agent acts through your actual systems — CRM, ERP, ticketing, database, internal APIs. We build the tool layer, handle authentication and permissions, and constrain what the agent can touch. Write access is granted deliberately, per action, never wholesale.
Human-in-the-loop design
Where a decision carries cost or risk, the agent proposes and a human confirms. We design the approval surface so confirming is fast, because an approval step people find tedious gets clicked through blindly, which is worse than no approval step.
Exception handling
The undocumented variants, surfaced during workflow mapping and given explicit behaviour — handle, escalate, or refuse with a reason. Most of the build time goes here rather than into the standard path, and a quote that assumes otherwise is a quote that will be revised.
Evaluation harness
A test set drawn from your real historical cases, with measured accuracy before launch and continuously after. This is the part most agent projects skip and the reason most of them cannot answer "is it working?" six months later.
Observability and cost control
Every run traced, token spend attributed per workflow, alerting on failure and drift. Agents can fail quietly — looping, degrading, or becoming expensive — and unmonitored agents are the ones that get switched off.
Fallback and escalation paths
What happens when the agent is uncertain, a tool is down, or the input is malformed. Defined behaviour, not undefined behaviour.
What you receive
Three things you keep
All three stay with you. The scope contract and the case set in particular are portable — they are what lets a different team, or a different vendor, verify the agent still behaves as agreed.
The scope contract
A written statement of every tool the agent can call, every action it can take, every condition under which it stops and asks a human, and who approves what. This is the document your security review reads, and it is what you point at when someone asks whether the agent could have done something. The boundary is agreed before the agent runs rather than discovered afterwards.
The action ledger
Every run recorded end to end: the input it received, the tools it called and in what order, the decision it reached, the reasoning it recorded, what it cost, and whether a human overrode it. Exportable and queryable. In regulated functions this trail is frequently worth more than the automation that produced it, because it answers questions about past decisions that your current process cannot.
The disagreement record
Built during shadow mode and maintained afterwards: every case where the agent's proposed action differed from the action a person actually took, with the resolution and the reason. It is what turns "we think it is accurate" into a list somebody can read and argue with, and it is how the scope gets widened responsibly rather than optimistically.
How it works
Shadow first, then live
- Weeks 1–2
Workflow mapping
We sit with the people who do the work today and document how it actually runs, including the exceptions nobody wrote down. We identify decision points, required data, and the boundary of what the agent should be allowed to decide.
- Weeks 2–3
Baseline and test set
We assemble a set of real historical cases with known correct outcomes. This becomes the measure of whether the agent works. Building it before building the agent is what keeps the project honest.
- Weeks 3–6
Build
Agent logic, tool integrations, guardrails, approval surface. Evaluated continuously against the test set. Most workflows reach a usable state faster than expected and then spend the majority of the time on exception handling, which is where the real work is.
- Weeks 6–8
Shadow mode
The agent runs against live traffic without acting. Its proposed actions are compared to what humans actually did. Disagreements are examined. This step is skippable and we recommend against skipping it.
- Weeks 8–10
Staged rollout
Live on a subset with approval gates, widening as accuracy holds. Gates loosen where the numbers justify it, not on a schedule.
- Ongoing
Run
Monitoring, evaluation against fresh cases, adjustment as your processes change. An agent is a system that needs an owner, not a project that ends.
Ten weeks is typical for a first production agent. Simpler workflows land in six. The phase that reliably surprises clients is exception handling — the standard path works early enough to feel finished, and then the remaining variants take longer than the standard path did. Anyone promising a production agent in two weeks is describing the standard path only.
Technology
What we reach for
Technologies we work with and reach for where they fit the workflow. This is not an exclusive stack and none of it is a partnership — agent tooling turns over fast enough that the choice is made per build, against what you already run.
Orchestration: LangGraph, the Claude and OpenAI agent SDKs, and custom orchestration where a framework adds more complexity than it removes — which is often, and we will say so.
Models: Claude, GPT and Gemini for reasoning-heavy steps; smaller and open-weight models for classification and extraction where they perform comparably at a fraction of the cost. Most production agents are a mix, routed by step rather than one model doing everything.
Durable execution: Temporal or similar where a workflow spans hours, involves waiting on a human, or must survive a failure mid-run without repeating an action it already took.
Integration: your existing APIs and internal services, with credentials scoped per action rather than per agent.
Evaluation: LLM-as-judge for subjective steps, deterministic assertions for everything that can be checked exactly. Braintrust, LangSmith, or a purpose-built harness.
Deployment: your cloud, your VPC, your data residency requirements.
Where this applies
Strong on volume, weak on rarity
Strongest in operations-heavy functions with high volume and documented-enough process — support triage, claims and invoice handling, order and data reconciliation, compliance checks against a written rule set.
Weakest where volume is low, the process changes constantly, or a single wrong action is expensive and hard to reverse. In those cases a simpler assistive tool usually beats an agent, and costs a tenth as much. We would rather tell you that during scoping than build the wrong thing well.
Relevant industry contexts: fintech, healthcare, logistics and supply chain, retail and e-commerce.
Why us
One team, not three vendors
The usual way
- A strategy consultant, a development shop and a marketing agency, each scoped separately.
- Months spent getting three vendors to agree, and blame to allocate when something breaks.
- The model ships and adoption becomes somebody else's problem.
With Nexiour
- One team across strategy, engineering and growth. You skip the coordination tax entirely.
- We own the whole outcome — from the first audit to the system that is running.
- Delivery across the UK, UAE, Germany and India, with overlapping hours in four markets.
What it costs
One workflow at a time
Fixed scope, quoted after workflow mapping. We price one workflow at a time. Three things drive the figure.
Exception density
What proportion of your volume falls outside the documented path. A workflow where nine cases in ten are standard is a different build from one where the exceptions are the norm, and this drives cost more than volume does.
Systems the agent must act through
Reading from a system is cheap. Writing to one — with scoped credentials, reversibility and an audit trail — is not, and each additional system compounds it.
Consequence of a wrong action
How much approval, logging and reversibility the workflow requires. An agent that drafts a reply and an agent that issues a refund are the same technology and materially different engagements.
Build and run are quoted separately. Inference, infrastructure and monitoring are ongoing and scale with volume, so a quote naming only the build is hiding the second number. Multi-workflow programmes are sequenced rather than bundled, because the second agent is meaningfully cheaper than the first and you should get that benefit rather than pay a blended rate.
FAQ
Frequently asked questions
Traditional automation follows a fixed script and fails when the input deviates. An agent interprets context and decides among available actions, so it handles variation — unstructured text, ambiguous cases, missing fields. If your process is fully deterministic, conventional automation is cheaper and more reliable. Use an agent where the variation is the problem.
It depends entirely on the workflow, which is why we build the test set before the agent. Well-scoped classification and extraction tasks commonly exceed human consistency. Open-ended multi-step reasoning is less reliable and we design approval gates accordingly. Any vendor quoting an accuracy figure before seeing your data is guessing.
When the process is genuinely deterministic — use conventional automation, it is cheaper and more reliable. When volume is low enough that the build will not pay back. When the process is undocumented and changing, in which case the agent encodes a moving target. And when a single wrong action is both expensive and hard to detect, because approval gates on every action remove most of the benefit. We would rather establish this in a scoping call than three months in.
Defined by design: approval gates on consequential actions, confidence thresholds that route uncertain cases to a human, full action logging, and reversibility where the underlying system allows it. We design failure behaviour explicitly rather than hoping it does not occur.
The common outcome is the agent absorbing routine volume while the team handles exceptions and grows into higher-value work. We will not tell you a headcount number, because the honest answer depends on decisions you make, not on the technology.
Yes, with deliberately scoped permissions. Read access is straightforward. Write access is granted per specific action, logged, and typically gated behind approval initially. We do not deploy agents with broad write credentials.
Inference cost scales with volume and model choice, and is usually a small fraction of the labour it displaces — but it is not zero and it can drift. We model it at scoping, instrument it in production, and route cheaper models to steps that do not need frontier reasoning.
Usually, somewhat. Processes built around human judgement often have implicit steps that need to become explicit. This is frequently the most valuable part of the engagement, and occasionally the discovery that the process should be fixed before it is automated.
Depends on workflow volume and current cost. We model this during scoping with your numbers and tell you if the payback period does not justify the build.
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