Guide, Verify, Solve — Anirban Chatterjee, Sonar
As AI-driven development moves from experimentation to engineering, the primary challenge is 'verification debt'—the gap between the quality provided by LLMs and the high reliability required for critical production systems. The speaker argues that relying solely on human review (which can be compromised, as shown by a Wharton study) or the generating model itself is insufficient. A robust solution requires implementing a zero-trust, multi-layered verification regime within agentic development loops to ensure code quality across syntax, data flow, architecture, and control flow.
Key takeaways
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Implement Zero Trust Verification
15:32
Assume all code (human or AI) could come from anywhere. Verification must use a different methodology than the one that wrote the code to prevent inheriting blind spots.
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Adopt Multi-Layered Review
15:57
No single technique is sufficient; verification must combine computational review, LLM-driven reasoning, and other approaches to catch all types of issues (quality, security, compliance).
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Use ACDC for Agentic Loops
17:12
The proposed framework is the Agent-Centric Development Cycle (ACDC), which includes three phases: Guidance (providing constraints/guardrails), Verification (running independent checks in real time), and Solve (allowing the agent to remediate issues).
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Standardize Verification Across Projects
20:46
Organizations should establish a single, consistent verification platform that applies uniformly across all teams, projects, and AI coding tools to eliminate blind spots.
