Enterprise AI Engineering · AI Agents & Automation

    Turn business workflows into agent-enabled systems.

    Enterprise AI agents and agent-enabled workflows engineered with tool use, permissions, integrations, evaluation and human oversight: automation that acts inside the rules of the business, and leaves the consequential decisions with people.

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    Three layers of automation

    Three layers of automation, one decision per workflow step.

    Not every workflow should become an autonomous agent, and most should not. We map each step of a process to the lowest layer that handles it reliably, and we move a step up a layer only when evidence supports it.

    1. Layer 1

      Deterministic Automation

      Rules, scripts, workflow engines and RPA.

      When it is appropriate

      When the process is stable, the inputs are structured and every branch can be written down. It is the cheapest, most testable option and it should not be replaced by AI just because AI is available.

      Its limits

      Breaks on unstructured input, exceptions and any judgment the rules did not anticipate.

    2. Layer 2

      AI-Assisted Workflow

      AI recommends. A human approves or acts.

      When it is appropriate

      When the step needs interpretation of language or documents, when the cost of an error is high, or when the organisation is not yet ready to let a system act alone. Most enterprise workflows should start here.

      Its limits

      Throughput is bounded by the people in the loop; the design must make review fast.

    3. Layer 3

      Agentic Workflow

      AI plans and executes controlled actions using approved tools.

      When it is appropriate

      When the task spans several systems, the actions are well defined and reversible or approved, and evaluation has shown task success at the required level. Human escalation remains for anything outside the agent's permissions.

      Its limits

      Requires identity, permissions, state, traces and continuous evaluation, or it is a prototype.

    In brief

    AI Agents & Automation engineering turns business workflows into agent-enabled systems: AI that reads the trigger, reasons over context and state, calls approved tools and completes or escalates the task under defined permissions and human oversight. It is useful when a workflow spans several systems, involves judgment that rules cannot capture, and has a clear boundary for what a person must approve. It solves the limits of rules and RPA without handing uncontrolled autonomy to a model.

    How an agent-enabled workflow runs

    From trigger to a validated, escalatable action.

    Six stages, with the permission boundary drawn at the tools and the human boundary drawn at the action. Both are engineered, not implied.

    1. 01

      Trigger

      An event, a ticket, a message, a schedule or a person's request.

    2. 02

      Agent

      A defined identity with a scoped permission set and a registry of approved tools.

    3. 03

      Reasoning and state

      Planning over retrieved context, with durable task state so work can be resumed or safely abandoned.

    4. 04

      Approved tools

      APIs, systems and integrations the agent may call, each classified by side effect.

    5. 05

      Action

      A record updated, a case routed, a draft prepared, a request submitted.

    6. 06

      Validation and human escalation

      Automated checks on the result, and a designed hand-off to a person for anything consequential or uncertain.

    Capabilities

    What we engineer.

    From discovering which steps should be automated at all, to the operating discipline that keeps agents inside policy after launch.

    • Workflow discovery and automation design

      Mapping the process, the exceptions and the decision points, then choosing which layer each step belongs to.

    • Agent architecture

      Planning patterns, state and memory, model routing and the boundaries between agent, tools and people.

    • Tool and integration layer

      APIs, enterprise system connectors and MCP integrations where relevant, with versioning and ownership.

    • Permission and identity model

      The agent as a known principal with scoped, auditable access per task.

    • Human-in-the-loop design

      Approval points, review interfaces and escalation paths designed for speed and accountability.

    • Evaluation and simulation

      Task-level test cases and simulated environments run before every change.

    • Monitoring and AgentOps

      Traces, task success, cost per task, incident handling and policy enforcement in production.

    • Migration from rules and RPA

      Replacing brittle automations step by step, keeping deterministic logic where it still wins.

    Where agents earn their place

    Credible use cases, with people kept where they belong.

    Examples of workflows where agent-enabled automation has a clear return. In every one, consequential actions in regulated or high-risk steps stay with a person; the agent prepares, routes and executes only what it is permitted to.

    • Customer operations

      Case summarisation, intent triage, draft responses and status updates across CRM and ticketing, with agents acting on approved cases.

    • IT support

      Ticket classification, runbook execution for known issues, access requests within policy, and escalation of anything novel.

    • Finance operations

      Invoice matching, exception investigation, reconciliation preparation and audit-trail generation, with approvals kept with people.

    • Procurement

      Supplier document extraction, policy checks on requests, and preparation of comparisons for a buyer to decide.

    • Sales operations

      Account research, CRM hygiene, proposal drafting from approved content, and pipeline updates from meeting notes.

    • Engineering

      Code review assistance, incident summarisation, test generation and change documentation inside existing tooling.

    • Knowledge workflows

      Answering policy and process questions with citations, and routing what the knowledge base cannot answer.

    • Document workflows

      Extraction, classification, validation against rules, and routing of contracts, claims and forms.

    • Compliance assistance

      Evidence collection, control mapping and first-draft assessments for a compliance professional to review and sign.

    • Manufacturing support

      Maintenance ticket enrichment, quality-deviation summaries and work-order preparation from sensor and shift data.

    Answer first

    The questions that decide whether an agent is safe to deploy.

    What is an enterprise AI agent?

    An enterprise AI agent is software that uses a model to plan and carry out multi-step work across business systems, operating as a known identity with explicit permissions, keeping state across steps, producing traces of what it did, and escalating to a person for anything outside its remit.

    The definition is deliberately operational. An agent that lacks identity, permissions, traces or an escalation path is not enterprise-ready, however capable the model behind it.

    AI agent vs chatbot?

    A chatbot answers. An agent acts. A chatbot produces a response for a person to use; an agent calls tools, updates systems and completes tasks, which is why it needs permissions, state, evaluation and oversight that a chatbot does not.

    AI agent vs RPA?

    RPA replays fixed steps on structured screens and breaks when the input or the interface changes. An AI agent interprets unstructured input, handles variation and chooses among approved tools. RPA remains the better choice for stable, fully specified processes; agents are for the steps that need judgment.

    Most organisations end up with both, and the design question is which layer each step belongs to.

    When should an agent be autonomous?

    When the action is well defined, reversible or low-consequence, the agent's task success has been demonstrated on a realistic evaluation set, and monitoring can catch and reverse mistakes quickly. Autonomy is granted per action type, not per agent, and it can be withdrawn.

    When should a human approve an agent's action?

    Whenever the action is irreversible, financially or legally consequential, affects a customer or employee materially, or falls in a regulated or high-risk workflow. In those cases the agent prepares and the person decides, and the interface is designed to make that decision fast and informed.

    We do not design fully autonomous decisions into regulated or high-risk workflows. Human controls stay in the loop by design.

    How should agent permissions work?

    The agent should act as its own identity with the minimum access each task needs, granted through a registry of approved tools classified by side effect, scoped per task where possible, logged on every use and reviewed like any other privileged account.

    Reusing a developer's or a service account's broad credentials is the most common and most dangerous shortcut.

    What is human-in-the-loop AI?

    Human-in-the-loop AI is a design in which people review, approve, correct or take over at defined points in an automated workflow. It is a control and a data source: every correction improves the evaluation set and, where appropriate, the system.

    How should enterprise agents be monitored?

    With a trace of every step, tool call and decision; task-level success and failure analysis; cost per task; escalation and unsafe-action rates; and alerting tied to policy. This is the AgentOps discipline, and it should exist before the first agent reaches production.

    AgentOps: operating agents in production

    Responsible AI in context

    Permissions, auditability and risk classification are part of the build.

    Every agent we ship is classified by the risk of the actions it can take, runs under a reviewed permission set, keeps an audit trail of every step, and has human oversight at the points the classification requires. We engineer these controls and the evidence for them; the client's governance function owns the policy.

    For the enterprise governance framework an AI capability center should own, see Responsible AI governance under AI GCC.

    Metrics we design for

    What we instrument on every agent-enabled workflow.

    • Task success rate

      Share of tasks completed to the agreed outcome on the evaluation set and in production.

    • Straight-through versus escalated

      How much work completes without a person, and whether the escalations were the right ones.

    • Tool-call correctness

      Whether the agent called the right tool with the right arguments, measured per step.

    • Time to resolution

      Elapsed time per task compared with the baseline process.

    • Cost per task

      Model, tool and human-review cost per completed unit of work.

    • Incident and unsafe-action rate

      Any action outside policy or permissions, and how quickly it was detected and reversed.

    These are the metrics we instrument and report. Targets are agreed per system against a measured baseline; we do not publish universal improvement percentages.

    Next step

    Map where agents belong in your workflows, and where they do not.

    Bring a process that consumes skilled people's time. We will map each step to deterministic automation, AI-assisted work or an agent, define the permissions and approval points, and describe what production would take.

    Map My AI Automation Opportunity Send us the use case in writing

    A 30-minute working session with a senior AI engineer. Bring the workflow, the data it depends on and the constraint that has stopped it so far.

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