AI Agent Development
Agents that do real work against real systems — with guardrails, observability and a cost ceiling.
An agent that answers questions is a demo. An agent that reads your CRM, drafts the reply, files the ticket and knows when to escalate to a human is a product. The difference is tool integration, guardrails and evaluation — not a better prompt.
We build agents that operate against your actual systems, with permission boundaries, retry and fallback behaviour, structured logging of every tool call, and hard spend limits so a runaway loop cannot produce a runaway bill.
What's included
- Agent architecture with tool and function-calling design
- Integrations to your CRM, helpdesk, database or internal APIs
- Guardrails, permission boundaries and human handoff
- Evaluation suite measuring accuracy against real cases
- Token and cost controls with per-tenant budgets
- Tracing and logging for every agent decision
Core capabilities
How we work
- 1
Workflow selection
We pick one workflow with measurable ROI rather than boiling the ocean, and define what 'correct' means before building.
- 2
Tools & integration design
Map the systems the agent must read and write, and the permission boundary around each.
- 3
Build & evaluate
Iterate against a scored evaluation set of real cases, so quality is measured rather than eyeballed.
- 4
Deploy & monitor
Ship behind spend limits and tracing, with a dashboard showing accuracy, cost and escalation rate.
Frequently asked questions
What is the difference between an AI agent and a chatbot?+
A chatbot answers from a fixed knowledge base. An agent takes multi-step action — calling tools, querying systems and writing back to them. If the task needs more than one step or touches another system, you need an agent.
How much does it cost to build an AI agent?+
A single scoped production workflow typically starts around $15,000. Cost is driven by how many systems the agent must integrate with and how strict the accuracy requirements are, not by the model itself.
How do you stop an agent giving wrong answers?+
Three things: retrieval grounded in your own data, guardrails that constrain what the agent may do, and an evaluation suite scored against real cases before release and monitored after. We report accuracy as a number, not an impression.
Which models do you use?+
We select per workload and keep the integration model-agnostic, so you can switch providers as pricing and capability change without rewriting the application.
Tell us what you're building
We'll assess the idea, the technical requirements and the fastest realistic path to production — and tell you honestly if we're not the right fit.