{"slug": "what-is-specification-driven-development-with-coding-agents-lessons-from-40", "title": "What is specification driven development with coding agents? Lessons from 40+ successful builds", "summary": "GoML deployed more than 40 AI systems into production in 2026 using specification driven development with Claude Code, an approach that externalizes intent into persistent Markdown spec files committed alongside source code rather than relying on conversational chat history. The team argues that vibe coding and conversational prompting degrade as context grows, causing implementation drift, while versioned specs survive session restarts and provide a review gate per phase. GoML notes the discipline requires no specialized framework and aligns with Anthropic's recommended Explore, Plan, Implement and Commit pattern.", "body_md": "The velocity that AI agents bring to virtually any use case can never be overstated. One prime use case is its utility as coding agents. With one prompt and a general sense of direction, anybody anywhere can generate working code - but for it to work meaningfully within a codebase is a different matter altogether. Piecing together chunks of code requires careful oversight and rarely works in the first go. Specification driven development is the way to make sure you get it right the first time, all the time.\n\nIn the year 2026 alone, we at GoML have deployed 40+ AI systems into production using specification driven development with Claude Code, so here’s a distilled guide with all of our learnings and insight.\n\nVibe coding is the term for when building software is done by assembling prompt-generated code, rather than thorough system-design level thinking.\n\nConversational coding depends on the engineer to hold the entire system design in mind while patching different bits of code together. As changes cascade across the variety of modules, iterative patching often produces friction that makes the codebase clunky.\n\nThe conventional loop of AI-assisted engineering is pretty straightforward:\n\nFor isolated scripts, single-function utilities or CSS adjustments, this dynamic works reasonably well. This mechanism falls apart once an agent transitions from autocompleting functions to implementing multi-tiered features.\n\nIn real-world codebases for important software functions, features almost always span multiple files and alter shared abstractions while demanding precise error handling. When you rely solely on natural-language prompts, context gradually degrades as the conversational history grows.\n\nThe fundamental issue in modern AI-assisted engineering/coding is how do you manage to preserve intent throughout the session. Without a persistent anchor, the model makes micro-decisions based on immediate context windows rather than overall architecture.\n\nThis- causes the implementation to drift away from the system's actual requirements.\n\nSDD reorganizes an AI coding session around three documents instead of one verbose conversation:\n\nThe exact form of a specification varies across the industry and context. Some organizations adopt strict JSON schemas, while others rely on GitHub Spec Kit or similar tooling.\n\nIn practice, SDD requires no specialized framework or heavy toolchain. The discipline functions reliably using standard Markdown documents committed directly to the project repository alongside the source code. The core premise remains the same, which is you externalize the intent into persistent files such that it isn’t reliant on the temporary memory of each chat session. This is its winning differentiator.\n\nSDD verifies that the feature does what the requirement says, across files and services that no unit test records. Vibe coding misses out on this\n\n| Dimension | TDD | BDD | Vibe coding | Spec driven development | \n|---|---|---|---|---|\n| Primary artifact | Unit tests | Given-When-Then scenarios | Natural language prompts | A written spec on disk | \n| Scope | One function | Cross-functional behaviour | Whatever the prompt covers | Feature-wide architectural intent | \n| Where truth lives | The test suite | Workshop notes | Chat history | The versioned spec file | \n| Validation | Automated test run | Human reference | Manual review, if any | Review gate per phase, plus tests | \n| Survives a session restart | Yes | Partly | No | Yes | \n\nIt is not necessary that every spec needs to be a permanent artifact. Although, you must decide upfront where a given piece of work sits.\n\nSDD is not a workaround you are bolting onto Claude Code. In fact, Anthropic's own team recommends similar workflows. They themselves recommend the EPIC pattern which stands for Explore, Plan, Implement and Commit.\n\nWhile Anthropic’s EPIC pattern helps lay the groundwork, true specification driven development takes that logic one step further. Plan Mode operates entirely inside active session memory - which basically means it vanishes the moment you clear the prompt history or restart your terminal. It offers a single review checkpoint before code generation begins.\n\nSpecification driven development takes the intent of Plan Mode and turns it into persistent batch of repository files - enabling longer context work. By writing the specification and plan directly to disk, the work survives resets and also introduces distinct review checkpoints between all stages of execution.\n\nYou do not need external orchestration frameworks or complex tooling to run this workflow. Claude Code natively supports persistent specification driven engineering through four built-in extension points.\n\nFirst, your project configuration file serves as the anchor. Placing references to active specifications and global design standards directly inside this configuration ensures that any new agent instance immediately respects existing architectural patterns.\n\nSecond, Plan Mode allows the agent to inspect dependencies, verify file trees and draft the implementation plan without generating any code.\n\nThird, the agent can spawn dedicated subagents to review generated diffs or execute discrete tasks from a clean context window - and thus, totally preventing conversational drift and hallucinating.\n\nFinally, automated hooks tend to enfoce deterministic “completion” criteria.\n\nClaude first explores the codebase in read-only mode, gathers the relevant context, and only then drafts the implementation plan.\n\nThe plan then turns that exploration into a concrete sequence of steps, files, and verification checks.\n\nThis keeps the work 'grounded' before any code changes happen. This is the key difference between guessing and planning. Claude inspects the codebase first, then converts that understanding into an implementation roadmap.\n\nPlan Mode separates discovery from implementation, helping Claude build a grounded plan before any files change.\n\nThe progression toward specification driven development was forged directly through our production builds over the past three years. Early in 2025, before native planning features existed in Claude Code, we engineered an end-to-end report generation engine that orchestrated data ingestion, context retrieval, synthesis and final document assembly. Mostly because the platform lacked innate ways to pause and structure execution, we had to enforce planning through manual conventions.\n\nThen, as native planning features arrived, we relied heavily on SDD to build Proxure’s spend analytics platform - where the application required translating freeform prompts into reliable SQL and complex data exports, all while maintaining state across iterative queries. Plan Mode gave the agent the operational pause it needed to map out multi-step logic safely within an active session.\n\nThe real inflection point came with GoML’s work for HealthOrbit, where we automated clinical documentation pipelines encompassing template creation, entity extraction, data validation and compliance governance. Given the zero-tolerance necessity for workflow disruption in healthcare/clinical environments, conversational prompting and in-memory planning were insufficient. Anchoring Claude Code to repository-native specifications gave the agent immutable boundaries for every regulatory requirement and data contract.\n\nLooking back across these systems, the takeaway is quite clear - every one of them would be built with full specification driven development today. When an architecture demands cross-stage validation, strict domain governance and persistent operational intent, early coding velocity matters far less than systemic control.\n\nThe prime value of specification driven development lies in prioritizing system design over perpetual debugging. Contrary to popular belief, software engineering with AI does not remove the need for technical mastery, if anything it elevates it’s significance if the goal is to deliver dependable systems that work at scale.\n\n`", "url": "https://wpnews.pro/news/what-is-specification-driven-development-with-coding-agents-lessons-from-40", "canonical_source": "https://dev.to/muthali/what-is-specification-driven-development-with-coding-agents-lessons-from-40-successful-builds-5815", "published_at": "2026-09-26 03:51:31+00:00", "updated_at": "2026-09-26 04:30:05.733543+00:00", "lang": "en", "topics": ["ai-agents", "ai-tools", "developer-tools", "large-language-models", "mlops"], "entities": ["GoML", "Claude Code", "Anthropic", "GitHub Spec Kit"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/what-is-specification-driven-development-with-coding-agents-lessons-from-40", "markdown": "https://wpnews.pro/news/what-is-specification-driven-development-with-coding-agents-lessons-from-40.md", "text": "https://wpnews.pro/news/what-is-specification-driven-development-with-coding-agents-lessons-from-40.txt", "jsonld": "https://wpnews.pro/news/what-is-specification-driven-development-with-coding-agents-lessons-from-40.jsonld"}}