Anthropic, OpenAI & Thoughtworks on Context Engineering
The core challenge in deploying AI agents is shifting from model intelligence to context engineering. Speakers from Anthropic, OpenAI, Thoughtworks, and Tessl argue that the surrounding context—including organizational knowledge, structured guides, and robust feedback loops—is the primary multiplier for agent capability. Key technical concepts include defining new constraints (human time, attention, context window), building specialized harnesses using computational tools like codemods and static analysis, and establishing a Context Development Lifecycle (CDLC) that runs parallel to the traditional Software Development Lifecycle (SDLC).
Key takeaways
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Context Engineering Multiplies Intelligence
0:24
Model intelligence alone is insufficient for durable, scalable products. Context engineering provides the necessary domain-specific knowledge required for agents to succeed within an organization.
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Remaining Software Constraints
5:02
Most traditional software engineering constraints are obsolete. The three remaining foundational limits when using human-agent teams are: human time (the most scarce resource), human/model attention, and the context window size.
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Agent Harness Architecture
8:41
A coding agent harness requires two components: 'guides' that proactively point the agent forward, and 'sensors' that provide immediate feedback for self-correction (e.g., static analysis, logs).
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The Context Development Lifecycle (CDLC)
Humans must own the CDLC while agents handle the SDLC. This involves generating context, evaluating agent performance via runtime observability, and optimizing skills in a continuous loop.