
The Real Python Podcast Constructing and Judging Modern Agentic Workflows
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Jul 10, 2026 Suneet Malhotra, an independent practitioner-researcher and quality engineer with ~18 years in test automation, shares practical work on specification enrichment and LLM-as-judge methods. He discusses building enrichment loops, using LLMs to triage outputs, measuring agreement with Cohen's kappa, and saving reusable specs for pipelines. Practical tools and a GitHub reference implementation are highlighted.
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Agents Accelerate End To End Product Workflows
- Agents can automate stages across the product lifecycle from PRD writing to test generation, boosting productivity by roughly 3x to 4x.
- Suneet Malhotra uses MCP to let agents pull live context from tools like Confluence and Figma instead of pasting documents manually.
Keep MCP Capabilities Narrow And Goal Oriented
- Avoid flooding an agent with all company data; instead provide narrowly focused tool endpoints and concise goals to prevent generic "AI slop."
- Suneet recommends limiting MCP-exposed capabilities to task-specific actions and relevant context only.
Run Specification Enrichment Before Writing Code
- Do run a specification enrichment loop before coding so the AI surfaces edge cases (printer offline, email bounces, session issues) the initial design misses.
- Suneet scores designs 0–1 and blocks pipeline progression until the design meets a threshold (eg. 0.7) via iterative AI questioning.

