AI agents that take real action,
with guardrails, not guesswork
Tool and function-calling agents that read, write, and act across your existing systems, built with human-in-the-loop checkpoints, retries, and fallbacks so they don't go off the rails in production.
Agentic workflows that actually
finish the task.
An agent that only works in a controlled demo isn't an agent, it's a liability. We build for the cases where tools fail, inputs are messy, and a human needs to step in.
Tool & Function-Calling Agents
Agents that call your APIs, databases, and internal tools to complete multi-step tasks, with structured outputs, retries, and clear error handling at every step.
Human-in-the-Loop Checkpoints
Approval steps before high-stakes actions, sending emails, processing payments, or updating records, so the agent assists, not gambles.
Multi-Agent Orchestration
Specialised agents coordinated through a planner/orchestrator pattern, for workflows too complex for a single prompt to handle reliably.
Business Process Automation
Replace manual, repetitive workflows, lead qualification, data entry, report generation, with agents wired into n8n, Make, or custom pipelines.
Agent Monitoring & Observability
Full trace logging of agent reasoning and tool calls, so when something goes wrong, you can see exactly why, not guess.
Customer Support Agents
Agents that triage tickets, answer from your knowledge base, and escalate to a human when confidence is low, not a chatbot that loops in circles.
06Internal Ops Agents
Agents that handle internal operations, scheduling, reporting, data reconciliation, freeing your team from repetitive coordination work.
07Agent Reliability Audits
Already have an agent that misbehaves in production? We audit the failure modes and add the guardrails it's missing.
An agent demo and a production agent are not the same thing
Agents that work in a controlled demo often fail the first time a tool call returns something unexpected. We design for that from the start, not after the first incident.
Guardrails by Default
Action limits, approval checkpoints, and scoped permissions so an agent can't take an action it shouldn't.
Built-In Retry Logic
Tool failures and malformed outputs are handled with retries and fallbacks, not a silent crash.
Full Trace Visibility
Every reasoning step and tool call is logged, so you can audit exactly what the agent did and why.
Full Code Ownership
The agent logic, prompts, and orchestration code are yours, no proprietary platform lock-in.
From task definition to a deployed agent in 4 stages
Every agent build follows the same process, because reliability comes from disciplined design, not a clever prompt.
Define, Build, Guard, Deploy
Define the task, tools, and boundaries
We map exactly what the agent should and shouldn't be able to do, and which tools it needs access to.
Build the agent and tool integrations
Tool/function-calling logic, structured outputs, and orchestration built around your actual systems.
Add checkpoints and stress-test failure cases
Approval steps, retries, and edge-case testing, including what happens when a tool call fails or returns garbage.
Ship with full trace logging
Deployed with monitoring and trace logs so you can see what the agent is doing in production, always.
AI Agents Development, Frequently Asked Questions
Answers to the questions US & UK clients ask us most.
What are AI agents and how do they differ from chatbots?
AI agents are systems that can take actions, not just chat. Where a chatbot answers questions, an AI agent can call tools, query APIs, update records, and complete multi-step tasks on its own, with guardrails and human-in-the-loop checkpoints. As an AI agent development company, we build agents that actually do work inside your business.
What can a custom AI agent automate for my business?
Common use cases include customer support resolution, lead qualification, data entry and CRM updates, report generation, document processing, and internal operations. If a task follows rules and touches your software, an AI agent can usually automate most of it.
Do you build AI agents with n8n, or with custom frameworks?
Both. For fast workflow automation we use n8n, Make, and Zapier; for complex, production-grade agents we build custom solutions with frameworks like LangChain and the OpenAI and Anthropic tool-calling APIs. As an AI automation agency we recommend the lightest tool that reliably does the job.
How much does AI agent development cost?
Cost depends on the number of tools the agent connects to, the complexity of the workflow, and reliability requirements. Simple automation agents start small; multi-step production agents are scoped as projects so you know the cost before we start. Book a free consultation for an estimate.
How do you keep AI agents safe and reliable in production?
We add guardrails, permission limits, validation steps, logging, and human-in-the-loop approval for sensitive actions. Agents are tested against real scenarios before launch and monitored after, so they stay reliable as your data and needs change.