AI Agents
AI Agents & Agentic AI development
Give repetitive, multi-step work to agents, with checkpoints.
In one paragraph
AI agents are software systems that use large language models to plan, call tools and complete multi-step tasks (researching prospects, resolving tickets, reconciling data, drafting reports) with people approving where it matters. We design, build and operate single agents and multi-agent systems for sales, support, finance and operations teams that want work done rather than just answered.
Why it pays off
- Work completed, not just drafted
- Consistent execution across systems with a full audit trail
- Fewer hand-offs and shorter cycle times
- Autonomy expanded only as metrics justify it
- Team time redirected to judgement work
What's included
AI Agents: everything in this service
Autonomous AI agents
Tool-using agents that plan, act and verify across your APIs to complete a defined job with minimal supervision.
Multi-agent systems
Teams of specialised agents (researcher, writer, reviewer, executor) coordinated by an orchestrator for complex workflows.
AI executives & copilots
Role-specific copilots that brief, draft, analyse and prepare decisions for leaders and specialists.
Orchestration systems
Routing, state, memory, retries and human checkpoints for agents running in production.
Planning & reasoning agents
Agents that break down goals, choose tools and self-check results for tasks that need more than a single prompt.
Web automation agents
Browser-using agents that fill forms, gather data and complete tasks on sites without APIs, with guardrails.
Research agents
Market, competitor and due-diligence research agents that gather sources, verify and produce cited briefs.
Finance agents
Reconciliation, variance analysis, invoice matching and reporting agents for finance teams, with approval steps.
Scheduling agents
Agents that negotiate times, book resources and manage changes across calendars and booking systems.
Coding agents
Agents that write, test and review code within your repositories and CI, scoped to defined tasks.
AI operations agents
Agents that watch dashboards, triage alerts, run playbooks and file tickets for IT and ops teams.
Use cases
Where it creates value
Automated reporting
Agents pull data from systems, produce the weekly report with commentary and flag anomalies for review.
Ticket resolution
Resolve routine support tickets end to end and escalate the rest with a summary and suggested action.
Data extraction and enrichment
Gather and structure information from documents, websites and databases into your CRM or warehouse.
Customer onboarding
Collect documents, verify details, configure accounts and keep the customer informed without manual chasing.
Market research
Continuous, cited research on competitors, prices and regulation delivered as briefs.
Enterprise operations
Cross-system processes such as approvals, procurement and compliance checks, executed by agents with audit trails.
How we deliver
How we deliver ai agents
- 01
Job definition
Define the job, inputs, tools, success criteria and the actions that need approval. Agents get narrow scopes first.
- 02
Tool and data access
Build the tools and connectors the agent will use, with scoped credentials and dry-run modes.
- 03
Agent build and evaluation
Implement planning, memory and orchestration; evaluate on real cases against a scored test set.
- 04
Supervised operation
Run with human approval, compare outcomes, tune prompts and tools, then relax approvals for proven paths.
- 05
Scale and monitor
Observability, cost limits and alerts, plus regular reviews to add capabilities safely.
Related work
Outcomes, not demos.
Frequently asked
AI Agents: questions we get
A chatbot answers questions in a conversation. An agent has a goal, can call tools (search, databases, APIs, browsers) and works through multiple steps to complete a task, checking its own results along the way. Many systems combine both: a chatbot front end with agents doing the work behind it.
Scoped credentials, dry-run modes, approval steps for irreversible actions such as payments or deletions, cost and step limits, and full logging. Autonomy is expanded only after the metrics justify it. Every production agent has an owner, an alert and a kill switch.
Reliable enough for well-defined jobs with good tools and evaluation, and unreliable when asked to do vague things. We invest most of the effort in tool design, test cases and fallbacks, and we report success rates honestly. Where a deterministic workflow will do, we build that instead.
Mostly LangGraph or lightweight custom orchestration in Python, with the Model Context Protocol for tools where it fits. Framework choice matters less than clear tools, evaluation and observability. We avoid heavy abstractions that make debugging harder. Where you already have a standard, such as Semantic Kernel, CrewAI or a Vertex or Bedrock agent runtime, we work within it rather than adding another layer.
Yes, when it is given good data, clear qualification rules and a human reviewing outreach at first. It researches accounts, personalises messages, handles replies and books into calendars. We keep compliance (opt-outs, sending limits, disclosure) built in. In the first weeks a human approves every message; once reply quality and meeting rates are proven, approvals are relaxed for the templates that perform reliably.
A focused agent for one job typically takes four to eight weeks from discovery to supervised production, depending on integrations. Multi-agent systems take longer and are usually delivered in stages so value lands early. The biggest schedule risks are access to systems and test data, so we ask for both in the first week and prototype against them immediately.
Ready to chart your AI course?
A free 30-minute consultation: we listen, ask hard questions and tell you honestly where AI will and won't help.