# How to Build Quality Gates into Agentic Coding Workflows — Nnenna Ndukwe, Qodo AI

## Executive summary

This workshop details how to implement structured quality gates into agentic coding workflows, moving beyond simple local testing. Using a sample FastAPI payments application (SignalPay), the speaker demonstrates a comprehensive process that integrates planning, local verification, and automated code review (PR review) using tools like Qodo/Codto. The core principle is operationalizing context—codifying standards, rules, and skills—to ensure AI agents adhere to defined quality and governance standards across the entire Software Development Life Cycle (SDLC).

## Key takeaways

- Structured AI Coding Workflow: A robust workflow requires defining and codifying code quality standards (rules) and expected behavior (skills) in a central context management layer, rather than relying solely on individual repository knowledge.
- The Quality Gate Process: The process involves multiple gates: 1) Planning (creating high-level and build session execution plans); 2) Local Verification (using deterministic tools like linters, Pytest, Bandit); 3) PR Review (Codto/Qodo analysis); and 4) Remediation (using agent skills to automatically resolve identified issues).
- Operationalizing Context: The system must ingest rules and skills from a central source (context management layer) to apply them consistently during planning, code generation, and code review, ensuring the agent is aware of all relevant constraints.

## Technical details

- Workflow Setup & Tools: The demonstration uses a SignalPay FastAPI payments API. Prerequisites include Git, GitHub CLI, Python, UV, Node.js/npm, and a coding agent (e.g., Codeex, Claude). The workflow utilizes Qodo/Codto for centralized rule and skill ingestion.
- Planning and Execution: The planning phase requires generating both a high-level plan and a detailed build session execution plan. These templates must specify the expected behavior, applicable rules, required skills, and the 'Definition of Done' (DoD) to guide the agent.
- Local Verification Gates: Local checks must be deterministic and include tools like linters, Pytest, Bandit, and static analysis (e.g., using `grep`) to ensure local contracts are preserved before PR submission.
- Code Review and Remediation: When a PR is opened, the platform (Codto) analyzes the code against ingested rules (e.g., 'Python functions must all have a doc string attached to it'). Issues can be automatically resolved using a dedicated agent skill (PR resolver), completing the code review loop.

## Practical implications

- Implement a structured process for defining and storing engineering standards (rules and skills) outside of individual repositories to ensure consistency across teams.
- Mandate a multi-stage verification process that includes local deterministic checks, agent-driven planning, and automated PR review.
- Utilize agent skills to automatically resolve identified code issues (remediation) rather than treating a passing local run as the final sign-off.

## Topics

AI Coding Workflows, Code Quality Governance, DevOps Automation, Context Management, FastAPI, Qodo/Codto, SignalPay, Packmind

Source: https://www.youtube.com/watch?v=-AKPRSYccJc