AI Coding Agents Need Specs, Not Just Prompts

AI coding agents now edit repositories, demanding a structured specification beyond simple prompts. A five-point framework outlines the necessary context, constraints, and validation evidence for effective machine execution.

AI coding agents are moving beyond simple queries to directly modify codebases, running commands, and producing branches. This shift elevates the importance of the specification that precedes the work, requiring explicit context, boundaries, and success criteria for effective machine execution. A structured spec, rather than an informal prompt, becomes the assignment between human intent and AI agent execution.

Five Points for an AI Agent Spec

The blog post by Pando85 outlines five minimum requirements for a robust coding-agent spec, arguing that it must carry the context, boundaries, and success criteria an agent needs. First, it must provide the context behind the task, explaining the underlying problem the agent is solving. Second, it defines the behavior that should change, detailing the specific modifications or new functionalities expected. Third, the spec specifies constraints the agent should preserve, ensuring critical elements of the existing codebase, such as API contracts or performance characteristics, remain untouched. Fourth, it includes examples or scenarios that define correctness, offering concrete benchmarks or test cases for the agent's output. Finally, it details the validation evidence a reviewer should inspect, establishing clear criteria for human oversight and sign-off.

This framework draws on established engineering practices like spec-driven development, behavior scenarios, and lightweight design documents. Methodologies such as OpenSpec and GitHub Spec Kit are cited as useful implementations of this pattern. The core idea is to provide the agent with sufficient context to act, and the team with enough structure to review the result, ensuring alignment between human intent and machine execution.

Prompts vs. Specs: Stability for Review

The author distinguishes between private prompts and team-visible specs, emphasizing their differing roles in the workflow. Private prompts, often used in immediate chat sessions, are optimized for quick interaction. They can contain shorthand, missing context, and assumptions understood only by the author. While suitable for local explanations or throwaway scripts, this informality becomes a liability for collaborative team engineering work. Private prompts typically disappear from the workflow after the agent starts, making them difficult to use as objective review criteria or to compare against a pull request. They also hinder future team members from understanding the rationale behind a change, creating technical debt in documentation.

Specs, conversely, provide a visible, inspectable shape for the assignment that persists throughout the development lifecycle. They can reside in various forms: a repo-local spec, an issue with acceptance criteria, a Behavior-Driven Development (BDD) scenario, a small design note, a change proposal, or a detailed pull request description. The critical element is that they make context and review criteria explicit, offering a shared object for both humans and agents to inspect before, during, and after implementation. This shared structure ensures that the assignment remains clear and verifiable, regardless of who reviews the agent's output.

Overhead for Smaller Teams

The emphasis on formal specifications, while beneficial for larger engineering teams and complex projects, introduces potential overhead for solo founders or small, agile teams. The time investment required to craft detailed specs, complete with context, constraints, examples, and validation evidence, could slow down rapid iteration cycles. For simpler tasks or early-stage products, a more iterative, prompt-driven approach with immediate human feedback might prove faster. The article does not detail the optimal balance point where the complexity of the task necessitates a full spec over a refined prompt.

Agent Autonomy and Iteration Loop

The framework assumes a largely deterministic agent execution based on a well-defined spec. However, current AI coding agents often require an iterative dialogue. The article focuses on the initial assignment but provides less detail on how to manage the feedback loop when an agent fails to meet the spec, or when the spec itself needs refinement mid-process. Without a clear mechanism for agent-human collaboration during execution, even a perfect spec may not prevent multiple rounds of adjustments. Future iterations of this playbook would benefit from outlining strategies for dynamic spec adjustment and agent-driven clarification.

The transition to AI coding agents that directly manipulate codebases demands a shift from informal prompts to structured specifications. By explicitly defining context, desired behavior, constraints, examples, and validation criteria, teams can establish a shared understanding that guides agent execution and facilitates human review. This approach transforms AI agent interaction from a conversational exchange into a disciplined engineering assignment, critical for maintaining codebase integrity and team alignment.

The investor read

This signal points to a maturing landscape for AI development tooling, moving beyond exploratory prompting to structured engineering workflows. It suggests a growing market for platforms that facilitate the creation, management, and integration of formal specifications with AI coding agents. Investable opportunities exist in tools that reduce the friction of spec authoring, provide robust validation mechanisms for agent-generated code, or offer advanced human-in-the-loop iteration management. This shift indicates a move towards scaling engineering output with AI, rather than simply augmenting individual tasks, creating a foundation for more complex, enterprise-grade AI-driven development.