AI Agent Development Services
Agents that complete work, not just answer questions
What is AI Agent Development?
An AI agent differs from a chatbot in that it does something. Given a goal, it plans the steps, calls the tools and systems it needs, checks its own progress and either completes the task or hands it to a person with context. Researching a prospect across several sources, reconciling records between two systems, resolving a support request end to end - work that takes a person an hour of coordination.
Agents are powerful and they fail in ways simple systems do not. An agent that misreads a goal can take several wrong actions before anyone notices. So the engineering concentrates on boundaries: what the agent may and may not do, where human approval is required, how actions are logged, and how it recognises that it is stuck. Done well, that constraint is what makes autonomy safe enough to deploy.
What We Deliver
Conversational AI Agents
Assistants that handle enquiries end to end - answering from your knowledge base, looking up account data, booking appointments and escalating with full context when a person is needed. Available continuously without the staffing cost that implies.
Task Automation Agents
Autonomous agents that execute multi-step work such as research, data reconciliation, report generation and record updates across systems. They plan, act, verify and report, replacing coordination work that consumed hours.
Knowledge Base Agents (RAG)
Agents grounded in your documents, wikis and databases through retrieval-augmented generation, answering from your actual content with citations. Dramatically reduces fabrication compared with a model answering from memory alone.
Workflow Agents with n8n
Agents built on n8n to connect Slack, email, CRMs, databases and hundreds of other applications, automating workflows specific to how your business runs rather than forcing a generic process on it.
Multi-Agent Systems
Several specialised agents coordinating on a larger problem - one researching, one analysing, one executing, one checking quality. Suited to complex work where a single agent handling everything becomes unreliable.
Tool Use and System Integration
Giving agents controlled access to your APIs, databases and internal tools, with permissions, rate limits and audit logging. The integration layer is where agent reliability is largely won or lost.
Problems This Solves
“We cannot let it act without oversight”
Approval gates on consequential actions, hard permission boundaries, and full logging of every step the agent takes.
“It gets stuck or loops”
Step limits, progress checks and explicit failure handling mean the agent escalates to a person instead of continuing indefinitely.
“It answers from the wrong information”
Retrieval grounded in your own content, with citations, so answers can be traced back to a source and verified.
“Running costs are unpredictable”
Step budgets, caching and routing simple tasks to smaller models keep per-task cost within a known range.
How We Work
Scope the Task
Define exactly what the agent handles, and what it must escalate.
Design Boundaries
Set permissions, approval gates, step limits and logging.
Build and Evaluate
Implement tool access and test against realistic scenarios, including failure cases.
Deploy Supervised
Launch with human review, then widen autonomy as reliability is demonstrated.
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