AI Agent Development.
Single agents and multi-agent workflows, safely bounded.
Agents that complete real work, alone or as a coordinated multi-agent workflow, within permissions enforced in code.
About AI Agent Development
An agent that can do anything will, eventually. We build ones that can only do the right things.
AI Agent Development is about building autonomous or semi-autonomous systems that complete real work such as booking, drafting, routing and resolving inside a bounded action space. We start with a single agent where that is sufficient, and add specialised agents only when the task benefits from separate responsibilities.
The design work isn't "give it more tools." It's the opposite: what CAN'T this agent do, and how do we make that a compile-time property, not a prompt-time hope? In a multi-agent workflow the same question covers orchestration: how agents share a plan, what state each one sees, how conflicts get resolved and how the run terminates.
Every agent we ship has a tool schema, a permission model, a rollback story, and a human gate on anything irreversible. Multi-agent runs add step budgets, cost caps and loop detection, with one trace an operator can pause, inspect and replay. The speed only matters if the boundaries hold.
Project deliverables
What we can deliver.
These are possible deliverables for this service. Your statement of work defines the selected scope, price, milestones, acceptance checks and responsible reviewers.
- 01Production agent with typed tool interface
- 02Multi-agent orchestration with typed messages, where justified
- 03Role-scoped permissions per agent & human gate on irreversible actions
- 04Full trace log & replay across every agent
- 05Cost caps, step budgets & loop detection
- 06Evaluation harness with adversarial prompts
Business use cases
Where AI Agent Development fits.
Explore practical applications of AI Agent Development and the workflows each can support. We agree the implementation scope around your users, systems and priorities.
Inbox & workflow triage
Route, tag, draft, or escalate, with a human sign-off on send.
Ops automation
Diagnose incidents, propose remediation, execute only after a human approves.
Multi-step case resolution
Planner and role-specific agents work one shared plan across departments, with a named human owner and a reviewer before anything is sent.
Technical approach
Capabilities your project needs.
We select tools around your requirements, existing systems and operational constraints. These capabilities guide the architecture and implementation.
- Tool interfaces
- Permission models
- Multi-agent orchestration
- Human-in-the-loop
- Trace & replay
- Adversarial eval
Our delivery process
From requirements to handover.
We plan requirements, architecture, design, implementation, testing, release and handover. The proposal identifies the engineering review and client acceptance points for your project.
Service questions
AI Agent Development: common questions.
How do you stop an AI agent from taking a wrong or irreversible action?
By limiting what it can do in code, not in the prompt. The agent can only call typed tools it has been granted, scoped to its role, and anything consequential or irreversible waits for human approval. Exact values, such as a price or a balance, are calculated and checked in code, not by the model. Before release, an evaluation set with adversarial prompts tests the cases that matter, and every tool call is logged in a trace an operator can inspect, pause or replay. If a step cannot be expressed as a bounded tool with a clear approval rule, it stays with a person.
Do we need a single agent or a multi-agent system?
Usually a single agent first. A single agent uses one decision loop and a set of tools. A multi-agent system separates responsibilities across roles, such as a planner, specialists and a reviewer, each with its own tools, permissions and evaluation. More agents do not automatically mean better results; we split roles when specialisation or independent review justifies the extra coordination, latency and cost.
What happens when an agent gets stuck or loops?
Step budgets, cost caps and loop detection stop the run, then a configurable escalation path takes over. Default: the session halts and pings a named human queue with the full trace attached. Every step, prompt, tool call and response stays replayable.
Plan your project
Discuss your requirements.
Tell us your goals, current systems and delivery priorities. We will define the scope and provide a written quote for AI Agent Development before implementation.
Timelines, ownership, third-party licences, warranty and support are agreed for your engagement.