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Catalogue › Data & Artificial Intelligence › AI Agents & Workflow Copilots
Data & Artificial Intelligence
AI Agents & Workflow Copilots
Task-oriented agents that reason over approved knowledge, use governed tools and coordinate multi-step business workflows under explicit permissions and oversight.
- Reduced handoffs and cycle time
- Consistent execution of repeatable processes
- Auditable tool use
- Human escalation at defined decision points
- Agent role and boundary design
- Tool and API orchestration
- Memory and knowledge controls
- Approval, exception and escalation patterns
- Agent evaluation and red-team testing
- Observability, cost and policy enforcement
- Discovery and approved solution requirements
- Configured or developed product release
- Integration and data-migration outputs where included
- Functional, security, performance and user-acceptance evidence
- Administrator and user documentation with training
- Go-live, warranty and support-transition package
- Single agent or multi-agent workflow
- Human-in-the-loop review
- Voice or chat interface
- Private deployment
- Managed agent operations
- Edition, modules and user/location count
- Hosting, environments and availability target
- Data migration and integrations
- Configuration versus custom development
- Security, compliance and assurance scope
- Training, support and service level
Content on this page comes from the governed ARRIX catalogue record DAI-05; pricing is confirmed only through a reviewed quotation.
What This Is
AI Agent & Workflow Automation Engineering
AI Agent & Workflow Automation Engineering builds governed AI agents that interpret a business objective, use the organisation's own data and systems, execute approved actions and coordinate multi-step work under human oversight.
The problem this answers
Someone has demonstrated an agent that books, updates or resolves something by itself, and the business now wants that across a real workflow. The demonstration ran against a copy of the data, with a person watching every step and quietly correcting it. Nobody has yet decided which actions the agent may take without asking, what happens when it misreads the objective, who is accountable for what it does inside a system of record, or how anyone would find out that it had gone wrong. The distance between that demonstration and a workflow the business can hand over is made almost entirely of those unmade decisions.
Work that used to wait on a person keeps moving
Multi-step processes that stall between systems are carried forward as cases arrive, with people brought in at the points where judgement or authority is genuinely required.
The limits of the automation are a decision, not a discovery
Which actions run unattended, which need approval and which are always escalated is agreed with the process owner before build and enforced in code rather than in instructions.
Capacity rises without a matching rise in headcount
Routine case handling, chasing and reconciliation are absorbed by the agent while the team keeps the exceptions, which is the work that needed them in the first place.
Automation reaches processes that rule-based tooling never could
Cases that vary in wording, format and sequence are interpreted rather than pattern-matched, so work previously judged too irregular to automate becomes reachable.
Accountability survives the automation
Every action is attributed, logged and reversible, so the process owner can still answer who did what, on whose authority and on what basis.
Mistakes stay contained instead of compounding
Action limits, verification steps and step budgets stop one misread objective becoming a long run of wrong records.
Why It Matters
Where this changes the outcome
An agent is not a chatbot with ambition. It is software granted the ability to act, and an agent with write access to a system of record can do damage at machine speed, creating, changing or approving records faster than any person will notice. That is not an argument against agents; it is the reason the engineering discipline exists. What makes an agent safe to deploy is the same thing that makes it useful in production: a bounded set of tools, explicit limits on what may happen without approval, a record of every action taken, and a rehearsed way to stop it and undo what it did.
| Organisation | Need | What ARRIX does | Outcome |
|---|---|---|---|
| A shared services team handling a high volume of routine requests | Clearing the repetitive cases without losing control of the exceptions | An agent that completes standard cases end to end and escalates anything outside its defined limits | The team spends its time on cases that genuinely needed a person, and the routine queue stops growing. |
| An operations function whose process crosses several systems | Carrying work between systems that were never integrated with each other | An agent that reads from each system, decides the next step and writes the approved update | Hand-offs that used to wait until somebody noticed them are completed as they arise. |
| A finance or procurement team doing manual reconciliation | Investigating records that do not agree and correcting them | An agent that gathers the evidence behind each difference and proposes or applies the correction under approval | Differences arrive as a reasoned proposal with its evidence attached rather than as a spreadsheet to work through. |
| A service desk under a rising ticket load | Acting on tickets rather than only replying to them | A copilot that drafts the response and, where approved, performs the underlying change through the same controls a person would use | Tickets are resolved rather than acknowledged, and those needing a person arrive with the investigation already done. |
| A specialist team doing repetitive research and drafting | Assembling information across internal sources and producing a first version | An agent that retrieves, cross-checks and drafts, with the specialist reviewing before anything is issued | The specialist starts from a checked draft with its sources attached instead of from an empty page. |
| A regulated business considering agents for the first time | Establishing what may be automated safely and under what conditions | Design of the action boundary, approval model and audit trail before any agent is built | The organisation reaches a defensible position on agent use before it commits to building one. |
How It Works
The path, stage by stage
- GOAL
- CONTEXT
- REASON
- SELECT TOOL
- ACTION
- VERIFY
- ESCALATE / CONTINUE
Goal
The agent receives an objective expressed in business terms, together with the limits that apply to it.
Interpreted objective and applicable limits
Context
It gathers the records, data and knowledge the objective depends on, through governed access paths.
Grounded working context
Reason
It works out what needs to happen next and in what order, and whether that next step falls inside its authority.
Plan with an authority check
Select tool
It chooses from an explicit inventory of permitted tools and systems, with credentials scoped to that action alone.
Chosen tool and scoped access
Action
It performs the step: a read, a write, a call to another system, or a request for approval where the boundary requires one.
Executed or requested action, logged
Verify
It checks the result against the intended outcome before further work depends on it.
Verified result or detected failure
Escalate or continue
Where confidence, authority or verification falls short the work goes to a person with its reasoning attached; otherwise the loop continues.
Completed work or a human hand-back with context
Detail
For whoever has to sign it off
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What ARRIX delivers
- Assessment of the workflow as it is actually performed, including the exceptions and the undocumented steps people take
- Definition of the action boundary: what the agent may do unattended, what requires approval, and what is always escalated
- Agent design covering objective interpretation, planning, tool selection, action, verification and escalation
- Tool and system integration with scoped credentials and least-privilege access granted per action
- Grounding in the organisation's own data and knowledge, so the agent reasons from enterprise context rather than from general knowledge
- Memory design: what is retained, for how long, and to whom it may be visible
- Human approval and review built into the workflow where people already work rather than added afterwards
- Multi-agent orchestration where the task genuinely needs specialised agents coordinating, with boundaries and stop conditions defined
- Evaluation of task success, action correctness and escalation behaviour, before release and after it
- Runbook and handover so the owning team can operate the agent, adjust its limits and stop it
What you receive
- Workflow and action map, including the decision points that stay with people
- Agent architecture with tool inventory, permissions and the agreed action boundary
- Working agent integrated with the systems in scope
- Approval, escalation and hand-back paths in operation
- Action logging and audit trail covering objective, reasoning, tool use and result
- Evaluation suite covering task success, tool use and failure behaviour
- Containment and rollback procedure, tested rather than described
- Operating runbook and handover to the owning team
Technical benefits
- Tool access defined explicitly, so the agent can call only what it has been granted and not everything that happened to be reachable
- Read and write paths separated, with write actions carrying stricter approval and verification than retrieval
- Objective, plan, tool call, result and action recorded as a traceable sequence rather than as one opaque response
- Verification built into the loop, checking the result of an action against the intended outcome before anything is built on it
- Escalation and hand-back to a person implemented as a normal path through the workflow rather than as an error state
- Memory scoped deliberately: what is retained within a task, between sessions and across users, and what must never be
- Idempotent and reversible action design, so a repeated or incorrect action can be identified and undone
- Orchestration boundaries that stop an agent looping, spawning work without limit or running past its time and cost budget
Governance and security
- Least-privilege credentials per tool, scoped to the specific action rather than granted to the agent as a whole
- Write access granted deliberately and separately from read access, with the approval position recorded for each action
- Every action logged with the objective, the reasoning, the tool used, the result and the identity the action ran under
- Personal data the agent encounters handled under the applicable privacy obligations, including what may persist in memory or logs
- Prompt injection treated as a live threat: untrusted content the agent reads is never allowed to widen what the agent may do
- A tested stop and rollback procedure with a named person who can invoke it and the authority to do so
- Segregation of duties preserved, so an agent cannot both raise and approve the same transaction
Technology options
| Area | Candidates | How it is chosen |
|---|---|---|
| Agent framework | Orchestration frameworks for single-agent workflows, Multi-agent coordination frameworks, Direct implementation against a model provider's tool-calling interface | A framework accelerates the common path and brings its own failure modes; the choice follows how much control the action boundary needs. Naming one is never a partnership, reseller or authorisation claim. |
| Tool and system integration | Model Context Protocol servers, Existing enterprise interfaces behind a permission layer, Workflow and process automation platforms already in use, Direct database access under a read-only or scoped-write role | Model Context Protocol is an open standard rather than a vendor relationship. Where a system already has a governed integration path, reusing it beats standing up a second one. |
| Memory and state | Short-term working state within a single task, Vector or document memory for retrieved knowledge, Structured task state held in an operational database, Deliberate statelessness between sessions | Memory is a governance decision before it is a technical one: what an agent remembers about a person or a case is data the organisation now holds and must account for. |
| Human approval | In-line approval inside the system the work already lives in, An approval queue with the agent's reasoning attached, Threshold-based approval by value, sensitivity or confidence | Approval placed where the approver already works gets used; approval in a separate tool gets skipped, and a skipped approval step is not a control. |
| Evaluation and control | Task-level success evaluation against real cases, Tool-call correctness checking, Step, time and cost budgets per task, Stop control and action reversal | Control mechanisms are built alongside the agent, because retrofitting them means changing how the agent acts. |
When you may not need this
If the process is stable, high in volume and fully describable as rules, conventional automation will run it faster, cheaper and more predictably, and putting an agent on top of it adds interpretation risk for no gain. You also do not need this where the work is only finding and summarising information, because that is a retrieval and generation problem rather than an action problem. And where nobody in the business will accept accountability for what the agent does inside a system of record, the honest position is not to deploy one until that owner exists.
Common Questions
Answered plainly
Will the agent operate on its own?
It operates inside limits you set, and those limits are part of the design rather than a temporary restriction. You decide which actions run unattended, which require approval and which are always handed to a person, and the agent enforces that boundary in code rather than in instructions. No honest supplier should describe an agent as fully autonomous, because the useful ones are deliberately bounded and the unbounded ones are the ones that cause incidents. What changes over time is where you place the boundary, and that should move on evidence of how the agent has actually behaved.
What happens when it gets something wrong?
It will get things wrong, so the design assumes it rather than hopes otherwise. Verification steps check the result of an action against the intended outcome, uncertainty routes to a person instead of to a confident guess, and every action is logged with the reasoning that produced it. Containment matters as much as accuracy: scoped credentials, step and cost budgets and a tested rollback procedure limit how far a wrong decision can travel before somebody intervenes. Agents cannot be made infallible, and the engineering discipline is about bounding the consequences of their errors.
How is this different from the automation we already have?
Rule-based automation follows a path someone specified in advance and stops when reality does not match it. An agent interprets the objective and the case in front of it, which is why it can absorb work that varies in wording, format and sequence. That interpretation is also the risk, which is why the tools, permissions and approval points are constrained explicitly rather than left open. Where your process really is describable as rules, existing automation is the better answer and ARRIX will say so.
Can we start small?
Starting small is usually the right sequence, and the useful first step is rarely the whole workflow. A common start is read-only: the agent investigates, gathers evidence and proposes an action that a person applies. That builds a record of how well it reasons on real cases, which is the evidence you need before granting write access to anything. Widening the boundary afterwards becomes a decision supported by observed behaviour rather than by a demonstration.
Do we need several agents or one?
Most workflows are served better by one agent with the right context and a well-chosen set of tools. Several agents earn their complexity when parts of the work genuinely need different specialisation, different data access or different authority. Every additional agent adds coordination, more places for a mistake to originate and more difficulty in explaining afterwards what happened. ARRIX proposes the smallest arrangement that does the work and states plainly what the extra complexity would buy.
Who is accountable for what the agent does?
Your organisation is, in the same way it is accountable for what an employee does within delegated authority. That is why the process owner agrees the action boundary before build, and why every action is attributed and logged against an identity. ARRIX is accountable for engineering those controls to the agreed design, evidencing that they work, and handing over a system your team can operate and stop. Accountability that has not been assigned before go-live tends to get assigned during an incident.
What about the agent being manipulated through content it reads?
Content taken from documents, tickets, email or web pages is untrusted input, and instructions hidden inside it are a known attack path. The defence is not to detect every attempt but to make a successful one worthless: what the agent may do is defined outside the content it processes, so text it reads cannot widen its authority. Sensitive actions still require approval, and unusual patterns of tool use are monitored rather than assumed benign. This is a standing design constraint, not a hardening exercise done once.
Next Step
What ARRIX needs to know
These are the questions a scoped proposal answers. Bring what you can; the rest is established in the conversation.
- Which process do you want an agent to carry, and how is it performed today, including the steps nobody has written down?
- Which actions in that process would you allow a system to take without a person approving them first?
- Which actions must never be taken without explicit human approval, whatever the system's confidence?
- Which systems would the agent need to read from, and which would it need to write to?
- If the agent repeated the same mistake across many records, how would you find out, and how would you undo it?
- Who owns this process today, and would that person own the agent's behaviour once it is live?
- What does a correct outcome look like here, and is it measured now?
- Is the data and knowledge the agent would need already reachable and governed, or does it live in people's heads and inboxes?
- When the agent is uncertain, what should happen: stop, ask, or proceed and flag?