AI Engineering
AI Agent Development
Build AI agents that do more than chat: they gather information, call the right systems, complete multi-step tasks and know when to ask a person for help.
Introduction
Agents are software with judgement, so they need boundaries
An AI agent combines a language model with tools: search, databases, calendars, ticketing systems or internal APIs. Given a goal such as “prepare a renewal pack for this account”, it decides which steps to take and in what order.
That flexibility is powerful and needs structure. We design agents with explicit permissions, step limits, approval gates and complete logs so that the organisation stays in control of what the agent can see and do.
Business challenges
The problems this solves
Work spread across many systems
Simple requests require hopping between CRM, email, spreadsheets and portals, which consumes skilled people’s time.
Brittle rule-based bots
Traditional automation breaks when inputs vary slightly, so teams fall back to manual handling for anything unusual.
Unbounded autonomy concerns
Leaders worry, rightly, about an AI taking actions with financial or customer impact without oversight.
Difficult to debug
Without tracing, it is hard to understand why an agent chose a particular path or produced a particular result.
Proposed approach
How we approach it
We build agents incrementally, widening their autonomy only as evidence of reliability accumulates.
- Start with a read-only agent that researches and recommends, then add actions behind approvals
- Expose tools through narrow, well-described functions with validated parameters
- Apply the requesting user’s permissions to every tool call
- Set budgets for steps, time and cost, with graceful stop-and-escalate behaviour
- Trace every reasoning step and tool call for review, testing and audit
Core capabilities
What we deliver
Task planning
Breaking a goal into ordered steps, revising the plan when a step returns unexpected results.
Tool and API use
Secure function calling against your CRM, ERP, ticketing, calendar and document systems.
Multi-agent coordination
Specialised agents for research, drafting and checking that hand work to one another under an orchestrator.
Memory and context
Short-term working memory for a task and curated long-term memory for preferences and history.
Approval workflows
Human sign-off for sensitive actions such as sending external emails, issuing refunds or changing records.
Observability
Step-by-step traces, cost tracking and outcome metrics for every agent run.
Use cases
Relevant use cases
Examples of where this capability is commonly applied.
Account research assistant
Collect recent orders, open tickets and contract dates for a customer and produce a briefing before a review meeting.
IT service desk agent
Diagnose common access issues, reset permissions through approved APIs and escalate hardware faults with context attached.
Procurement intake agent
Read purchase requests, check budget codes and supplier status, and route complete requests for approval.
Recruitment coordination
Propose interview slots from panel calendars, prepare candidate packs and send confirmations after recruiter approval.
Technology considerations
Choosing the right technology
Agent frameworks change quickly. We keep business logic and tool definitions independent of any single framework so the solution remains maintainable.
Agent runtime
- Function / tool calling
- Orchestration graphs
- Task queues
Integrations
- REST and GraphQL APIs
- Webhooks
- Email and calendar APIs
- Internal databases
Controls
- Role-based access
- Approval steps
- Tracing and logging
- Rate and cost limits
Implementation
Implementation process
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1
Task analysis
Shadow the current process and list each decision, data source and action involved.
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2
Tool design
Define safe, narrowly scoped tools and the permissions each requires.
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3
Agent prototype
Build a read-only version and test it against recorded real scenarios.
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4
Controlled actions
Introduce write actions behind approvals and measure accuracy and override rates.
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5
Scale and govern
Expand scope as metrics allow, with regular trace reviews and policy updates.
Explore more
Related services and solutions
Intelligent Automation
Automation that combines rules, AI judgement and human review to streamline business processes.
Learn more about Intelligent AutomationWorkflow Orchestration
Coordinate multi-step processes across systems, teams and AI services reliably.
Learn more about Workflow OrchestrationGenerative AI Development
Language-model features that draft, summarise and extract information, grounded in your own content.
Learn more about Generative AI DevelopmentAPI Development & Integration
Well-designed APIs and dependable integrations that keep your systems and data in sync.
Learn more about API Development & IntegrationFAQs
Frequently asked questions
Answers to the questions we hear most often. If yours is not covered here, our team is happy to talk it through.
A chatbot mainly answers questions in conversation. An agent pursues a goal: it can look things up across systems, perform actions through tools and complete a multi-step task, often without a back-and-forth conversation.
Only if you decide it should, and typically only for low-risk, reversible actions after a period of supervised operation. Sensitive actions are routed for human approval by design.
Agents have step, time and cost limits. When they cannot complete a task with confidence, they stop, record what they tried and hand the case to a person with that context attached.
Yes, as long as there is a reliable interface: an API, a database view or a well-defined file exchange. Where none exists, we can build a small integration service first.
Explore an agent for your team’s busiest workflow
Tell us which multi-step task consumes the most time and we will assess whether an agent is the right fit.
