AI & Data
Autonomous AI agents that complete work inside your systems
We build agents that do more than chat: they plan, call your APIs, update records, and hand off to humans with a full audit trail of every step and tool call.
24/7
Coverage for support, triage and back-office queues
70%
Tier-1 requests resolved without human touch
<2s
Median first response on customer-facing agents
Full
Trace of every reasoning step and tool call
Definition
What ai agents means here
An AI agent is a governed software worker: a model with a defined goal, a permitted toolset (APIs, databases, browsers), memory, escalation rules and observability, so it can complete multi-step tasks without a human driving each action.
Capabilities
What is included
Each capability is scoped, estimated and delivered against agreed acceptance criteria.
Agent architecture
Single-agent, supervisor and multi-agent topologies chosen against your task complexity and risk profile.
Tool & API integration
Typed tool definitions over ERP, CRM, ticketing, warehouse and payment systems with least-privilege scopes.
Memory & context
Short-term state, long-term vector memory and per-tenant isolation.
Human-in-the-loop
Confidence thresholds, approval gates and clean escalation into your existing queues.
Evaluation harness
Scenario suites and replay testing so behaviour changes are caught before release.
Observability
Per-step tracing, cost per task, success rates and failure clustering dashboards.
Deliverables
What you receive
- Agent capability map and risk classification
- Tool catalogue with permission model
- Production agent runtime with tracing
- Scenario evaluation suite and regression pipeline
- Escalation workflows wired into your service desk
- Operating runbook and cost model
Engagement models
How we can work together
Fixed-scope project
Defined outcome, milestone billing and a signed delivery plan. Best when requirements are stable and the business case is approved.
Dedicated squad
A cross-functional pod (architect, engineers, QA, delivery lead) reserved monthly for a rolling roadmap with sprint-level reporting.
Managed service / AMC
SLA-backed run and evolve model covering monitoring, incident response, security patching and a monthly enhancement allowance.
Implementation roadmap
How the engagement runs
Indicative timeline for a single-entity engagement; multi-country programmes are phased.
- 01
Task selection · Week 1
Pick tasks with clear inputs, verifiable outputs and measurable volume.
- 02
Tooling · Week 2-3
Expose systems as safe, typed tools with sandboxed credentials.
- 03
Agent build · Week 4-6
Planning loop, memory, guardrails and escalation logic.
- 04
Shadow run · Week 7-8
Run alongside humans, compare outcomes, tune thresholds.
- 05
Autonomy ramp · Ongoing
Increase autonomy per task class as accuracy targets hold.
Comparison
Chatbot vs workflow automation vs AI agent
| Capability | Scripted chatbot | RPA / workflow | AI agent |
|---|---|---|---|
| Handles unseen phrasing | No | No | Yes |
| Takes action in systems | Limited | Yes, fixed path | Yes, planned per case |
| Adapts when a step fails | No | No | Yes, replans or escalates |
| Setup effort | Low | Medium | Medium-high |
Advantages
- Handles long-tail requests scripts can never cover
- Every action is logged, replayable and reversible
- Autonomy is dialled up per task class, not all at once
- Reuses the tool layer for future agents and copilots
Trade-offs to plan for
- Needs clean API access to the systems it must operate
- Requires ongoing evaluation as models and processes change
- Poorly bounded tasks produce unpredictable cost per run
Decision guide
Is this the right service for you?
Match your situation to the recommended starting point.
| If this sounds like you | We recommend |
|---|---|
| High volume, repetitive, rule-based tasks | Start with AI automation — cheaper and more deterministic. |
| Variable requests needing judgement and multiple systems | Agents are the right fit; begin with a shadow-mode pilot. |
| Only knowledge answers are needed | A grounded RAG assistant via LLM integration is sufficient. |
Industries
Where we apply ai agents
FAQ
AI Agents questions, answered
Related services
Often delivered together
AI Solutions
We take AI from boardroom ambition to audited production: use-case discovery, data readiness, model selection, evaluation harnesses and the MLOps needed to keep results stable after go-live.
AI Automation
We combine document AI, rules engines and workflow orchestration to remove manual handling from invoice processing, order entry, compliance checks and service triage.
LLM Integration
We embed LLM capability into products you already run — search, summarisation, classification, drafting — with the retrieval, caching and evaluation that keep it accurate and affordable.
Ready to scope your ai agents engagement?
Share your context and we will come back with an approach, timeline and indicative investment — under NDA.