Agentic Development Principles - Part 1

Source: Dev.to

Principles

Integrate AI tools directly into the coding environment

Integrate AI tools directly into the coding environment to deliver instant suggestions and error checking, minimizing context switching and delays. This maximizes value‑added time and aligns with B3: The Batch Size Feedback Principle.

Failure Scenario:
A team uses an AI code completion tool with a 5‑second delay. Developers either wait (breaking flow) or ignore the tool, resulting in inconsistent adoption and wasted potential.

Human review of AI‑generated output

Every AI‑generated output must pass final review by a domain expert who retains full accountability. AI accelerates delivery but introduces risk; human oversight ensures quality and prevents costly errors, supporting E1: The Principle of Quantified Overall Economics.

Failure Scenario:
An AI‑generated database query is deployed without review, causing performance issues and technical debt.

Use AI agents to break down large tasks into small, verifiable experiments

Use AI agents to break down large tasks into small, verifiable experiments (e.g., auto‑generated unit tests, code variations), reducing risk and enabling fast feedback. This applies V7: The Principle of Small Experiments.

Failure Scenario:
An AI generates a massive, brittle test suite. Maintenance overhead grows, slowing development and negating the benefits of automation.

Provide high‑quality context and select the appropriate model

Developers must provide high‑quality context and select the agent/model with the best cost‑benefit profile for each task. Effective prompt engineering and tool selection minimize waste and the Cost of Delay, echoing E16: The Principle of Marginal Economics.

Failure Scenario:
Using a slow, expensive model for a trivial task with a vague prompt leads to wasted time and resources.

Practice frequent, low‑stakes interactions with AI tools

Teams must practice frequent, low‑stakes interactions with AI tools to build expertise in prompt engineering and tool selection. Skill development reduces long‑term rework and accelerates learning, following FF11: The Batch Size Principle of Feedback.

Failure Scenario:
Developers receive initial training but lack time for practice, resulting in slow learning and inefficient workflows.

Scale AI agent autonomy by task complexity

Scale AI agent autonomy by task complexity: fully delegate repetitive, low‑risk tasks; use AI as a consultant for complex or high‑risk decisions. This balances velocity and risk, applying B4: The Batch Size Risk Principle.

Failure Scenario:
Delegating complex build optimization to AI leads to short‑term gains but introduces critical errors, increasing rework and risk.

Ensure robust automated safety nets before granting autonomy

Before granting full autonomy to AI agents, ensure robust automated safety nets (e.g., CI/CD pipelines, test suites) are in place to continuously validate outputs. Automation must be checked by automation to prevent catastrophic failures, supporting B4: The Batch Size Risk Principle.

Failure Scenario:
An AI refactors components with inadequate automated tests, introducing subtle bugs that escape detection until production.

Establish tiered governance

Establish tiered governance: the company sets strategic safety and cost policies, the team defines tactical goals and quality metrics, and the developer retains autonomy over tool selection and workflows. This decentralizes optimization while maintaining compliance and security.

Failure Scenario:
Mandating a single AI tool for all teams blocks specialized workflows, increasing risk and cycle time for critical tasks.

Resolve problems quickly to avoid compounding errors

If an AI interaction does not resolve the problem quickly, the likelihood of successful resolution drops with each additional interaction, as accumulated context and unresolved errors compound. Fast, decisive resolution is critical to prevent error propagation and cognitive overload, aligning with B3: The Batch Size Feedback Principle.

Failure Scenario:
A developer repeatedly prompts an AI agent to fix a bug, but each iteration introduces new minor errors and increases context complexity. After several cycles, the original issue is buried under layers of confusion, making resolution harder and increasing rework.