AI Agent Development

A chatbot answers questions. An AI agent takes action.

Where a chatbot might tell you the status of an order, an agent can investigate a delivery problem, contact the courier, update the customer record, and send a notification. Where a chatbot provides information, an agent gets things done.

This capability brings real productivity gains. Tasks that took staff hours can complete in minutes. Work that happened only during office hours can happen continuously. Processes that required coordination across systems can flow automatically.

Autonomous systems need guardrails. An agent that can take action can also take wrong action. Our approach builds in appropriate controls from the start.

Defined boundaries limit what agents can do. Each agent has explicit permissions: which systems it can access, which actions it can take, which decisions require human approval.

Audit trails record every action. You can see exactly what an agent did, when, and why. This supports compliance requirements and helps with troubleshooting.

Human checkpoints ensure oversight for consequential decisions. Agents handle routine work autonomously but escalate exceptions and high-stakes situations to people.

Graceful failures prevent cascading problems. When agents encounter unexpected situations, they pause and alert rather than guessing.

Our clients deploy agents for:

Customer operations: Processing returns, handling billing queries, managing subscriptions, coordinating deliveries. Work that follows patterns but requires accessing multiple systems.

Back office: Invoice processing, expense approvals, compliance checks, report generation. Administrative tasks that consume staff time without requiring judgement.

Sales support: Lead research, proposal assembly, contract preparation, follow-up scheduling. Letting salespeople sell instead of doing admin.

Technical operations: System monitoring, incident triage, routine maintenance tasks, alert management. Keeping systems healthy without constant human attention.

Agent development follows a structured process.

Workflow analysis documents the task in detail: inputs, steps, decisions, outputs, exceptions. We observe how work happens today and identify where agents add value.

Architecture design determines which components are needed: large language models for reasoning, traditional code for reliability, integrations for system access, orchestration for coordination.

Controlled deployment starts small. We pilot agents on limited scope, measure performance, and expand gradually. This catches problems before they affect broad operations.

Ongoing refinement improves agents based on real usage. We monitor performance, tune behaviours, and add capabilities as you identify new opportunities.

We build agents using appropriate technology for each situation. LangChain and similar frameworks for complex reasoning chains. Direct API integrations for reliable system access. Custom orchestration when standard tools fall short.

The technology choice depends on your requirements: some agents need sophisticated language understanding; others need rock-solid reliability; most need both.

Agent projects typically begin with a single, well-defined workflow. We prove value in a contained area before expanding scope. This approach manages risk while demonstrating what becomes possible.

Use these prompts to pressure-test whether an agent is the right approach and what it would take to deliver safely.

“Which steps in our workflow can be automated safely, and which steps should require human approval?”

“What systems and tools would the agent need access to, and what permissions would be acceptable?”

“What evaluation plan will prove this works: success metrics, edge cases, and failure modes?”