AI Agent Development
We design and build AI agents that plan, call tools, and complete multi-step tasks — with the guardrails and human checkpoints that make an agent safe to run in production.
Discuss your projectWhat you get
- Agent task and tool-use design
- Tool/function-calling integration with your systems
- Guardrails, approval checkpoints, and fallback behaviour
- Evaluation harness scoring agent runs against real tasks
- Monitoring for cost, latency, and failure modes
How we work
- 01
Scope
We define exactly which tasks and tools the agent is allowed to touch, and which need human approval.
- 02
Build
Tool-calling integration with your systems, with guardrails designed in from the start.
- 03
Evaluate
An evaluation harness scores the agent against real tasks before it goes near production.
- 04
Operate
Monitoring for cost, latency, and failure modes once the agent is live.
Outcomes
- An agent that completes real multi-step tasks, not just a chat wrapper
- Guardrails that keep the agent inside its intended scope
- Visibility into what the agent did and why, after every run
AI Agent Development: FAQs
What is the difference between an AI agent and a chatbot?
A chatbot answers questions. An AI agent plans and executes multi-step tasks — calling tools, taking actions in your systems, and adapting based on the result — with guardrails controlling what it’s allowed to do autonomously versus what needs human approval.
How do you stop an AI agent from taking a wrong or costly action?
We scope the agent’s available tools tightly, add approval checkpoints for higher-risk actions, and build an evaluation harness that scores agent runs against real tasks before anything reaches production.
Can an AI agent integrate with our existing internal tools?
Yes — agent tool-calling is built against your existing APIs and systems, so the agent acts through the same interfaces your team already uses.