Engineering brief
Why AI agents work for code but fail elsewhere—and what to do
At a glance
- Relevance
- Practical value
- Warnings
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Karan Vaidya argues coding agents succeed because Git, CI/CD, tests, and governance built trust. Knowledge work lacks these six primitives—centralization, history, context, verification, governance, reversibility.
The bottleneck for AI agents shifts from models to missing infrastructure for non-coding domains.
Summary
Karan Vaidya argues that coding agents succeed because software engineering already has essential infrastructure: centralized repos, history (Git), testing frameworks, governance (code owners, branches), and reversibility. These systems enable trust without blocking speed.
Knowledge work agents lack all of this. A deal is scattered across Salesforce, Notion, Gmail, and Slack. No central source of truth, no history, no automatic verification. This forces agents to stitch context manually and act blindly, making even simple tasks dangerous.
The six primitives he proposes—centralization, record/history, context, verification, governance, and reversibility—are structural, not model-driven. Without them, agents cannot be trusted with sensitive actions like email or financial transactions.
While Composio is building this platform, the talk highlights a real tradeoff: building infrastructure is harder than improving models. Teams should focus on creating sandboxed environments and logging before deploying agents beyond code.
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