Beyond the Model: Why Context and Human Checkpoints Define Agentic Engineering
Pavan_Belagatti · x · 2026-08-09
The author explores the core challenges of 'Agentic Engineering,' where AI agents independently handle full-cycle tasks like writing code, running tests, and opening PRs, potentially automating the entire SDLC.
However, the hardest part isn't the model itself, but building accurate context. To reason like a real engineer, an agent must understand the service catalog, ownership, infrastructure, and policies. Furthermore, to earn trust and mitigate risks, systems must integrate human checkpoints for policy validation and manual sign-off before executing risky actions.
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