Europe 2026
Platforms for Humans and Machines: Engineering for the Age of Agents — Juan Herreros Elorza
Overview
This talk focuses on engineering platforms to effectively support both human developers and AI agents. It argues that the challenges developers face in deploying applications and managing infrastructure become more pronounced and limiting when AI agents are involved. The core thesis is that by adopting specific platform engineering best practices, organizations can unlock greater productivity and enable AI agents to function more autonomously and effectively.
Who should watch
- AI Engineers
- Platform Engineers
- Product Managers
- Developers struggling with complex deployment pipelines
- Teams looking to integrate AI agents into their workflows
- Organizations aiming to improve developer experience and productivity
Key takeaways
- **Self-service is crucial:** Platforms must allow users and their agents to provision resources and perform actions independently, without requiring manual intervention or waiting for specific individuals.
- **API-first design:** Well-defined, discoverable, and schema-validated APIs are essential for AI agents to interact with platform capabilities securely and efficiently.
- **Local-first development:** Shift left by enabling agents to validate and perform tasks locally whenever possible, failing early to prevent wasted effort.
- **Clear instructions and verification:** Provide agents with precise task descriptions and explicit criteria for success, as they cannot rely on human-like visual inspection or intuition.
- **Agent-aware observability:** Ensure logs, metrics, and traces are accessible via APIs or CLIs, allowing agents to monitor and verify their own operations.
- **Structured documentation:** Keep documentation close to the code or centralize it, making it easily discoverable and consumable by agents, ideally through APIs.
- **Encourage contributions with guardrails:** Foster community contributions to platforms, but implement clear policies and agent-specific documentation (e.g., agent.md, skills) to maintain security, compliance, and standards.
- **Measure impact:** Track metrics related to application delivery, reliability, and platform usage (e.g., support requests) to validate the effectiveness of platform improvements for human and AI users.
Notable quotes
*Best practices are still best practices. They are just much more obvious and perhaps much more painful now that we have these coding agents working next to us.*
*Make it automatic, remove people from the process and make it easy to the greatest extent possible.*
*Take advantage. Everyone from the executive level to the individual contributors are looking at AI now. It is a very hot topic. So, you can use AI as the excuse to implement some best practices that again were always best practices if you didn't have the chance to do it until now.*
Unofficial community note. Prefer the recording for nuance.