Two engineers talk through a build at their screens

Practice 04 · AI Engineering

Put agents to work across the value chain.

Agents and AI-native products, built with your teams and run to production standard.

See the Levi's story

Why now

“The pilot impressed the board, and nobody uses it.”

Pilots test the wrong idea for too long.
Prototypes never reach production standard.
AI output cannot pass review, because nobody can trace what it did.

Capabilities

What we build.

Six disciplines, one team, from the first experiment to the agent your people rely on.

Rapid experiments
Test an idea, or drop it, on the evidence.
AI-native product engineering
Put agents to work across the value chain.
AI-native software engineering
Ship faster, with the review trail intact.
Agents and agentic workflows
Put agents to work, with a person approving.
Agentic quality engineering
Test the software and the AI.
AI product design
Design how people and agents work together.

How we work

Strategists first, then engineers.

Forward Deployed Strategists (FDS) prove an idea is worth building. Forward Deployed Engineers (FDE) build it to run.

01 · FDS
Forward Deployed Strategists (FDS)
- Frame one use case with your leaders
- Prototype it as a measured experiment
- Keep it, or drop it, on the evidence
02 · FDE
Forward Deployed Engineers (FDE)
- Build to production standard, in your environment
- A person approves where the stakes are high
- Hand over a team that can run it

See the full maturity ladder

Proof

Agents in production for our clients.

All case studies

Under the hood

How agents run in production.

Build, host and operate every agent on one governed path, and grow from single tasks to cross-functional systems.

Build, host, operate

Build
An agent accelerator platform
A pipeline every agent goes through: scan, build, test and publish.
- Policy enforcement
- Agent publishing
- Model usage guardrails
Host
Secure agents in the cloud
Deploy and run agents on a hardened foundation.
- Hosting
- Agent-to-agent (A2A) communication
- Agent-to-backend access through MCP
Operate
Governance to run within compliance
Audit, log and monitor every agent in production.
- AI governance policies
- Roles and responsibilities
- Change adoption and operating model

The pipeline: scan, build, test, publish, deploy, run, audit, log, monitor.

Agentic maturity, designed for scale

It’s important to design for scale with the optimum ratio of human to agent workforce from the onset. Too few agents per person can lead to underutilization. Too many agents per person can lead to overwhelm and burnout.

01
Individual augmentation
Agents automate basic tasks.
Platform
A secure, easy-to-use, single-user agent environment, such as a local app or browser extension
Data
The individual’s context: email, calendar, files and a standard knowledge base
AI
Capable foundation models for reasoning and instruction following, with retrieval
02
Workflow automation
Agents automate existing, low-complexity workflows.
Platform
A shared agent runtime with an API gateway for basic monitoring and logging
Data
Structured system data such as CRM and ERP, workflow data and schemas
AI
Small task-specific models for classification and extraction, with tool calling on enterprise APIs
03
Functional agentic workflows
Agents redesign team workflows.
Platform
A central orchestration layer with tools for hand-offs, audits and compliance
Data
Cross-system data streams, with feedback loops for learning and tuning
AI
Multi-agent systems with advanced planning and self-correction
04
Cross-functional agentic systems
Agents drive complex, cross-functional workflows and decisions.
Platform
Agent networks with governance, security, sandboxing and simulation environments
Data
Unstructured data, data mesh and federated access across domains
AI
Goal-driven agents with long-term memory and automated experimentation