Dispatches from O’Reilly: AI Coding Agents Prove Critical Need for Detailed Technical Specifications in Agile Development

As AI coding agents make software implementation cheaper and faster, detailed technical specifications are proving critical rather than obsolete. A vague prompt bypasses upfront planning, triggering expensive downstream correction loops, whereas upfront specification front-loads intent into repeatable, executable checks.

The Hidden Tax of Vague Prompting in Agentic Workflows

Software engineering was never primarily about typing code. It has always been about deciding what should exist, mapping out trade-offs, and defining what “done” means when software hits the real world. In traditional development, vague requirements ran into human friction. A reviewer caught an edge case, QA found an undescribed path, or a senior engineer held the real logic in their head.

Agents alter this dynamic by turning implementation into a zero-marginal-cost operation. When a team feeds an under-specified idea to a model, the agent generates a plausible system at machine speed before anyone agrees on what the system is supposed to do. A simple prompt feels tempting because it gets code running immediately. Then the correction loops begin.

You review output, clarify intent, ask for changes, rerun tests, and hunt for the next gap. Someone has to decide whether the output matches the real goal, turning that reviewer into an exhausted oracle.

By contrast, full formal specification takes real upfront effort. Writing acceptance criteria, contract tests, or behavior-driven development scenarios is tedious. But the downstream cost differs because an executable check never gets tired, rushed, or optimistic five minutes before lunch.

Why Specification Requires Its Own Validation Layer

Writing a spec is only half the battle. Specs can easily fail by contradicting themselves, covering only the happy path while ignoring retries, rate limits, or partial failure, or describing behavior that sounds precise but cannot be verified.

When an agent executes a flawed spec faithfully, diagnosis becomes far more difficult. The implementation looks coherent and may even pass checks, but the root problem lives upstream in the specification. Fixing it means unwinding code and reasoning together.

That is why specification validation deserves its own dedicated step before implementation begins. Teams need to ask whether a draft is internally consistent, complete enough for the task, and testable. As daily.dev notes, multi-agent AI coding pipelines compound these risks when interpretive drift sets in. Once one agent treats another’s output as ground truth without knowing it misunderstood a requirement, mistakes get buried under layers of competent-looking code.

Managing Context Rot and Scaling Agent Workflows

Stuffing more documentation into an AI coding agent’s context window does not improve reliability. Model performance degrades as input grows, a phenomenon documented by Chroma’s context rot research highlighted across developer communities. Mixing old design prose, stale tickets, outdated plans, and current implementation makes it unclear to the model which parts are active instructions.

Photo: daily.dev

This reality requires lean, structured constraints. The optimal balance for agentic work sits in the middle: enough structure to constrain the work, enough examples to make intent concrete, and enough executable checks that review stops being a guessing game. Well-designed, discoverable APIs allow agents to treat code itself as a reliable spec, while keeping documentation concise to prevent context rot.