# Unlocking AI innovation through automated governance

DevFeed: [Unlocking AI innovation through automated governance](<https://devfeed.tech/articles/unlocking-ai-innovation-through-automated-governance-19720.md>)

Original publisher: [Read original article](<https://deliveroo.engineering/2026/02/27/unlocking-ai-innovation-through-automated-governance.html>)

Author: Alex Evenson

Published: 2026-02-27T00:00:00Z

Content type: article

Language: en

Sources: [Deliveroo](<https://devfeed.tech/sources/deliveroo.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [MCP](<https://devfeed.tech/topics/mcp.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [agent observability](<https://devfeed.tech/topics/agent-observability.md>), [observability](<https://devfeed.tech/topics/observability.md>), [tracing](<https://devfeed.tech/topics/tracing.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-development](<https://devfeed.tech/tags/ai-development.md>), [automated](<https://devfeed.tech/tags/automated.md>), [governance](<https://devfeed.tech/tags/governance.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [observability](<https://devfeed.tech/tags/observability.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [sdk](<https://devfeed.tech/tags/sdk.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

## AI overview

Deliveroo describes building centralized AI infrastructure to combine innovation with governance and compliance. The article introduces its AI Agent Platform and AI Hub, including MCP integrations, workflow orchestration, model-provider abstraction, evaluation pipelines, agent tracing, storage, visualization, and configurable trace retention policies.

## Source excerpt

Over the past two years, we've witnessed a Cambrian explosion of AI development, with Generative and Agentic AI capturing stakeholders' attention. AI/ML Engineers are delivering real business value by intermixing LLMs and agentic systems with traditional machine learning systems. It's been an era of rapid prototyping and quick integrations. Teams have adopted different approaches - each unlocking new potential, but also introducing complexity. As these prototypes evolved into production systems, duplicated effort and inefficiencies began to show. Teams built similar observability and reporting solutions in parallel, governance efforts between engineering and legal teams rapidly increased in complexity, and compliance processes developed as bespoke solutions on a per-team basis. We realised that this complexity was limiting our efforts to scale AI projects across the company. To move fast and stay compliant, we needed consistent governance, shared tooling, and clear accountability across teams. We therefore built the components of our AI ecosystem around a central infrastructure - designed for both innovation and oversight. Built upon the principles of automation and transparency, this approach enables our engineers to build safer systems, follow streamlined governance processes, and ultimately ship features faster. Introducing our AI Agent Platform and AI Hub Our in-house AI Agent Platform deserves a blog post of its own, so we will keep it short for now. It is our foundation for developing, deploying, and operating agentic AI systems at Deliveroo. It includes MCP server integrations, handles workflow orchestration, abstracts over different model providers and includes an evaluation SDK for both offline and online eval pipelines. Our AI Agent Platform also gives our engineers full AI agent tracing out of the box, with data storage and user interface components for visualisation. Teams can configure their own data retention policies for these traces to comply with na