Agent Engineering
AI Agent & Agentic AI Development
We design and build custom AI agents, and the complete agentic system around them when the work calls for one. Orchestration, tools, state, memory, human approvals and the runtime it all executes in.
- 01INTENTThe goal arrives from a user, an event or another system.
- 02CONTEXTThe agent gathers the data and knowledge it needs.
- 03PLANIt reasons through the work and breaks it into steps.
- 04TOOL.CALLIt acts through a defined, permissioned tool.
- 05APPROVALSensitive actions wait for a person.
- 06RESULTThe work is completed, traced and evaluated.
What it is
An AI agent is not an LLM with a prompt. It is a system that understands a goal, reasons through a task, uses tools, reads and writes the business data it needs, and takes controlled actions inside defined policies.
We start from the work, not the model: what the task is, who does it today, which decisions and systems are involved, which actions can be automated and which need approval. Where a problem is better solved by deterministic software, we say so. The agent is positioned around the work it performs.
Some work needs one agent. Some needs a complete agentic system: several workflows, orchestration, shared state and approvals inside one controlled runtime. It is the same engineering at a different size, so it is one service. We prefer simple architectures, and add orchestration and more agents only when they create real value.
When you need it
- SIGNAL 01A task is multi-step, repetitive and currently done by people moving between systems.
- SIGNAL 02You need an agent that acts in your business applications, not a chat window that answers questions.
- SIGNAL 03Your team has a working prompt, but no agent that can be trusted with real tools.
- SIGNAL 04The agent must hand off to a person when it reaches the edge of its authority.
- SIGNAL 05The problem spans several workflows, systems and decisions, and needs orchestration and state, not only a model call.
What we build
The engineering.
- 01
Task-based and autonomous agents
Agents scoped to a job, from a single well-bounded task to long-running work that plans and executes over many steps.
- 02
Tool-using agents
Agent tool calling against your APIs, databases and business applications, with every action it can take defined up front.
- 03
Conversational and customer-facing agents
A conversational interface connected to the underlying workflow, so the agent can resolve a request, not only reply to it.
- 04
Human-in-the-loop agents
Approval steps and escalation paths wherever an action needs a person to sign it off.
- 05
Agent state and memory
Short-term state for the task in flight and longer-term memory where the work actually benefits from it.
- 06
Internal and enterprise agents
Agents for operations, engineering, research, finance and knowledge work inside the organisation.
- 07
Agentic system architecture
The right shape for the workload, from one agent to a supervisor and specialists, chosen for the problem rather than the trend.
- 08
Agent and workflow orchestration
Sequential, parallel and event-driven workflows, with handoffs between agents where the work genuinely calls for them.
- 09
Agent runtime design
The runtime the system executes in, designed for long-running work, recovery and scale.
How it works
One run, end to end.
- 01
INTENT
The goal arrives from a user, an event or another system.
- 02
CONTEXT
The agent gathers the data and knowledge it needs.
- 03
PLAN
It reasons through the work and breaks it into steps.
- 04
TOOL.CALL
It acts through a defined, permissioned tool.
- 05
APPROVAL
Sensitive actions wait for a person.
- 06
RESULT
The work is completed, traced and evaluated.
What it integrates with
Chosen for the workload and your environment — not a preferred provider.
- APIs
- Function tools
- MCP servers
- Databases
- CRM and ERP
- Ticketing
- Internal APIs
- Search
- Retrieval systems
- Model gateways
In production
Production is part of development.
Evaluation, security, deployment and operations begin before release — on this service as on every other.
EVAL
How we test it
- Task-success evaluation against the work the agent is meant to do.
- Tool-use testing, including correct tool selection and call accuracy.
- Workflow and trajectory evaluation across the whole run, not only the final answer.
- Regression tests that run on every change to prompts, tools or models.
POLICY
How we secure it
- Least-privilege permissions for every tool the agent can call.
- Human approval gates on actions that should not be automatic.
- Guardrails and prompt injection testing before release.
- Sandboxed execution and runtime isolation where actions are risky.
RUNTIME
How we deploy it
- SaaS, private, self-hosted, on-premises or air-gapped, as the environment requires.
- Agent and model versioning with a tested rollback.
TRACE
How we operate it
- Traces of every model call, tool call and handoff.
- Cost, latency and success rate tracked per task.
What you receive
Engineering outputs, not a deck.
We do not hand over a prototype and leave production engineering to you.
- 01A working agent or agentic system in your environment, built around the work it performs
- 02Architecture and tool definitions, documented, with the reasoning for each choice
- 03Orchestration, state and memory designed for the workload
- 04An evaluation suite with a golden dataset for the task
- 05Permission model and approval gates for every action
- 06Tracing and monitoring wired in before release
- 07A release pipeline with versioning and rollback