Context Is the New Code — Patrick Debois, Tessl

2026-05-03 · Source: AI Engineer · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, extended

Summary

This presentation introduces the concept of "Context as the New Code" and proposes a Context Development Life Cycle (CDLC) for AI coding agents, mirroring the traditional Software Development Life Cycle. The speaker, Patrick, argues that as AI agents generate more code from context, managing this context becomes paramount. The CDLC outlines four key phases: Generate, Test, Distribute, and Observe. Generating context involves human prompting, reusable prompts (like agent.md files), pulling in external documentation or repository data, and spec-driven development. Testing context moves beyond traditional code testing to include linting for context format, "Grammarly-like" checks for agent understanding, LLM-based judgment for adherence to conventions (e.g., API prefixes), and end-to-end tests within sandboxed environments. Distribution covers checking context into repos, packaging reusable context as "skills" or libraries, managing dependencies, and ensuring security through scanning and AI SBOMs. Finally, observing context involves analyzing agent logs for missing information, incorporating PR feedback, monitoring production code generated by agents, and implementing context filters and harness engineering for security and traceability.

Key takeaway

For AI Architects and Machine Learning Engineers building with coding agents, you should adopt a structured Context Development Life Cycle. This means moving beyond ad-hoc prompting to systematically generate, test, distribute, and observe your context. Implement robust testing, including LLM-based evals and end-to-end checks, to ensure context quality and agent adherence to standards. Your focus on engineering context will directly improve agent reliability and reduce production issues.

Key insights

Effective AI agent performance hinges on meticulously engineered context, necessitating a dedicated Context Development Life Cycle.

Principles

• Context is the new code.
• Treat context with software engineering rigor.
• AI agents are engines; context is their fuel.

Method

The Context Development Life Cycle (CDLC) involves generating, testing, distributing, and observing context. This includes creating reusable prompts, validating context with evals, packaging context as skills, and monitoring agent performance and security.

In practice

• Implement linting and "Grammarly-like" checks for context quality.
• Develop LLM-based tests for code generated by agents.
• Package reusable context into "skills" for distribution.

Topics

• Context Development Life Cycle
• AI Coding Agents
• Prompt Engineering
• AI Evals
• Skill Packaging

Best for: AI Architect, Machine Learning Engineer, AI Engineer, MLOps Engineer, Prompt Engineer

Related on AIssential

• My cheatsheet for a clean context — Effective coding agent use requires mindful context management, precise prompting, and systematic debugging.
• Podcast: Context is the Key to the Agentic Architecture Revolution: A Conversation with Baruch Sadogursky — Context engineering, not prompt engineering, is key to leveraging AI agents for software development by making specifications the source of truth.
• Context is the New Code — Effective AI system performance hinges on meticulously engineered context and prompts, treating them as architectural components.
• Context Engineering for Coding Agents — Effective AI agent performance hinges on optimized context management, particularly through dynamic, wiki-based memory systems.
• Organizational Context for AI Coding Agents with Dennis Pilarinos — Bridging the organizational context gap is crucial for effective AI coding agents and human developers.
• Presentation: State of Play: AI Coding Assistants — AI coding assistants are evolving towards greater autonomy, necessitating advanced context engineering and robust safety harnesses.

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Engineer.