Task agents
Focused agents that own one well-defined job end to end, such as triaging requests or preparing case files.
- Clear scope and tool permissions
- Structured outputs for downstream systems
- Fallback to human queues
Agentic AI
Agents that finish the job, with a human in the loop where it counts.
We build AI agents that read, reason, call your systems and hand off cleanly to people, so routine multi-step work gets done without losing control or auditability.
Overview
Agentic AI & AI Agent Development
An AI agent is software that can decide what to do next: look something up, call an API, draft a response, ask for approval. Used well, agents absorb the coordination work that sits between systems and slows teams down.
Used carelessly, they create new risks. Our approach puts boundaries first: explicit tool permissions, approval gates for consequential actions, full traces of every decision and evaluation suites that test behaviour, not just outputs.
Business challenges
Problems we are usually asked to solve
01
Work that falls between systems
Staff copy data from email to CRM to ERP and back again, because no single system owns the whole process.
02
Fear of autonomous mistakes
Leaders want the efficiency of automation but cannot accept an agent issuing refunds or changing records unchecked.
03
Opaque agent behaviour
When an agent gets something wrong, nobody can see which step, tool call or piece of context caused it.
04
Frameworks changing monthly
Teams hesitate to commit to an agent stack that may be obsolete before the project ships.
Our solutions
What we build and deliver
Multi-agent systems
Coordinated agents with distinct roles, such as researcher, reviewer and executor, for complex workflows.
- Supervisor and specialist patterns
- Shared memory and state management
- Conflict and loop detection
Agent tooling and integration
Secure connections between agents and your business systems, using standard protocols where possible.
- Model Context Protocol (MCP) servers
- Scoped API credentials
- Sandboxed code and browser execution
Oversight and observability
The controls that make agents acceptable to risk, compliance and operations teams.
- Approval gates for high-impact actions
- End-to-end tracing and replay
- Behavioural evaluation and red-teaming
Capabilities
Everything in scope
- Agent architecture and design
- Single and multi-agent systems
- Tool and function calling
- MCP server development
- AI copilots inside existing apps
- Voice and chat agents
- Human-in-the-loop workflows
- Agent memory and state
- Agent evaluation and red-teaming
- Tracing and monitoring
- Cost and latency optimisation
- Agent governance policies
Technologies
Tools we work with
- Orchestration
- LangGraphClaude Agent SDKOpenAI Agents SDKSemantic KernelTemporal
- Protocols
- Model Context ProtocolOpenAPIWebhooksgRPC
- Observability
- LangSmithLangfuseOpenTelemetryArize Phoenix
- Runtime
- PythonTypeScriptDockerKubernetesServerless
Technology choices are made per project based on your environment, skills and constraints.
Industry applications
How this applies in practice
Insurance & InsurTechClaims intake agents that gather documents, check policy terms and prepare assessor summaries.Logistics & Supply ChainException-handling agents that chase delayed shipments and update customers.Professional ServicesResearch agents that assemble briefing packs from internal and public sources.TelecommunicationsService agents that diagnose common faults and schedule technicians.
Engagement approach
How an engagement runs
- 01
Process mapping
We trace the current workflow, the systems involved and every decision point an agent would face.
- 02
Autonomy design
Together we decide which steps run autonomously, which need approval and which stay human.
- 03
Pilot in shadow mode
The agent runs alongside your team, proposing actions without executing them, until accuracy is proven.
- 04
Graduated rollout
Autonomy expands step by step as evaluation results and audit trails build confidence.
What is the difference between an AI agent and a chatbot?
A chatbot responds to messages. An agent pursues a goal: it can plan steps, call tools and systems, check its own results and decide when to hand work to a person.
How do you stop an agent from doing something harmful?
Agents only receive the tool permissions they need, high-impact actions require human approval, inputs and outputs pass through guardrails, and every step is logged for review. We also test agents against adversarial scenarios before release.
Do agents replace our existing automation?
Usually not. Rules-based automation is still the best choice for stable, predictable steps. Agents handle the variable, judgement-heavy parts and call existing automations as tools.
Which agent framework do you use?
We choose per project and keep business logic separate from the framework, so you are not locked in. For many projects a lightweight custom orchestrator is simpler and more reliable than a heavy framework.
Start a conversation
Got a workflow that eats your team's week?
Walk us through it. We will tell you honestly whether an agent is the right answer.