5 reasons why the future of software development is less coding, more documentation

tldr: Software development is evolving rapidly. The industry is moving from manual coding to AI-assisted development and automation. This creates unprecedented documentation needs. Documentation is shifting from a “nice-to-have” to a critical requirement for product success.

Here are five reasons why the future of software development is less coding, more documentation.

Infographic of why the future of software development is less coding, more documentation

1. The move towards platform engineering and reducing cognitive load

Presentation slide titled “Cognitive Load is the New Bottleneck” explaining how developer cognitive load limits productivity in large enterprises adopting Internal Developer Platforms (IDPs). The slide contrasts two funnel-style illustrations. On the left, labeled “AI-Assisted Velocity,” a wide funnel overflows with orange and gray fragments that bottleneck at a narrow exit labeled “Human Cognitive Capacity,” symbolizing information overload. On the right, labeled “Platform Engineering,” a structured funnel channels the same fragments smoothly through a highlighted orange pathway labeled “Living Knowledge System,” representing improved knowledge flow. Supporting text states that hard-to-use platforms cause investment failure and calls for moving from static manuals to living knowledge systems. A sidebar highlights “DORA 2024 Findings,” noting that documentation quality meaningfully connects to performance and user-centricity, sourced from the DORA 2024 Report by Google Cloud. Clean, modern infographic design with white background, orange accents, and emphasis on developer experience, documentation quality, and platform engineering.

Large enterprises are moving away from traditional DevOps towards platform engineering. Internal teams build “Internal Developer Platforms” (IDPs). These platforms provide self-service infrastructure, CI/CD, testing, security and observability.

The goal is to reduce cognitive load on developers. If the platform is difficult to use, multi-million pound investments fail.

Platform engineering treats internal platforms as products. This requires documentation with the same rigour as external product documentation.

Technical Authors create “golden path” documentation. These are step-by-step, frictionless guides. They show developers exactly how to deploy services without understanding underlying infrastructure. This is core to developer experience (DevEx).

In continuous delivery environments, the challenge shifts. It moves from “write a manual” to “continuously maintain a living knowledge system”. This system spans product usage, operational runbooks, troubleshooting and change communication.

 

Alt text: Presentation slide titled “Pillar 1: Engineering Velocity & The ‘Golden Path’” illustrating how structured documentation accelerates developer productivity. The slide features two large, gray maze diagrams side by side, separated by a vertical orange line. The right maze highlights a clear, labeled route called “The Golden Path,” symbolizing a guided, frictionless workflow for developers. On the left, text under “The Concept” explains that technical authors should create step-by-step guides enabling developers to deploy services without understanding underlying infrastructure. Below, “The Shift” emphasizes treating internal platforms as products with rigorous documentation standards rather than an afterthought. On the right, a callout contrasts a crossed-out approach—“Write a manual at the end of the waterfall phase”—with the modern approach: “Integrated control and Developer Experience (DevEx) enabler.” Clean, modern enterprise design with white background, orange accents, and strong focus on engineering velocity, golden path workflows, platform documentation, and developer experience.

The 2024 DORA findings emphasise that documentation quality connects meaningfully to performance and user-centricity. This contradicts industry slogans sometimes misinterpreted as de-prioritising documentation.

Documentation is shifting from a milestone to an integrated control. It moves from waterfall-style “end phase” to continuously updated content. This reduces variability and risk in delivery and operations.

2. API-first development and advanced specifications

APIs are the primary product for many companies. The 2025 Stack Overflow developer survey reported 90% of developers preferred API/SDK documents as their primary learning resource.

Simple Swagger documentation might not be enough. New standards like the Arazzo specification and AsyncAPI require expert architectural writing. Arazzo documents multi-step API workflows. AsyncAPI handles event-driven architectures.

Technical Authors optimise “time to first call”. This measures how long third-party developers take to make their first successful API call. It’s a key metric for API sales and developer adoption.

3. AI governance, compliance and ethics requirements

Alt text: Presentation slide titled “Governance & The Regulatory Landscape” highlighting the role of technical documentation in regulatory compliance for high-risk AI systems. The slide features a large circular compliance badge on the left with a bold orange checkmark and the text “EU Artificial Intelligence Act – August 2025,” symbolizing regulatory approval and certification. On the right, explanatory text states that technical documentation is now a condition of sale and that high-risk AI systems cannot enter the market without it. A section labeled “Mandates” lists key requirements: “Risk Assessments & Data Lineage,” requiring mandatory tracking of data origins; “Model Cards (Annex IV),” calling for detailed explanations of how models were trained; and “Technical Transparency,” emphasizing audit-ready and legally cleared documentation. A highlighted insight box concludes that technical authors now bridge the gap between legal requirements and technical reality. Clean, modern enterprise slide design with white background, orange and gray accents, and strong focus on AI governance, compliance, and regulatory documentation.

The EU AI Act entered into effect in August 2025. Various US state laws also apply. Software companies face unprecedented regulatory pressure.

High-risk AI systems must have technical documentation before market release. This documentation must be kept up to date. Compliance requires technical transparency documentation.

Required documentation includes risk assessments, model cards and data lineage reports. Model cards explain how models were trained. (See Annex IV of the AI Act for complete requirements.)

Technical Authors bridge the gap between legal requirements and technical reality. They ensure AI products are legally cleared for market. This is auditable technical writing.

4. Software supply chain security and transparency

Alt text: Presentation slide titled “Security & Supply Chain Transparency” explaining how documentation enables software security and regulatory compliance. The main visual shows an exploded, blueprint-style diagram of a software container cube with visible internal components such as gears, servers, data packets, and modules, representing a containerized application and its dependencies. Orange arrows connect internal components to an SBOM (Software Bill of Materials) table on the right, listing component names, versions, licenses, and cryptographic hashes, including examples like libSSL, kernel-core, auth-service-api, and network-stack packages. Supporting text emphasizes that security transparency is now the standard, noting that around 90% of organizations face regulations such as the Cyber Resilience Act or DORA. Additional text highlights a mandate requiring documentation of ICT risk management and incident reporting by December 2027, with the stated goal of creating “audit-ready” documentation that proves control over every dependency in the software supply chain. Clean, technical infographic design with white background, orange accents, blueprint measurements, and strong focus on SBOMs, software supply chain security, compliance, and documentation transparency.

Security documentation is no longer optional. Transparency is the new standard. Around 90% of organisations fall under one or more regulatory requirements.

The EU Cyber Resilience Act requires documentation of control frameworks. From 11 December 2027, companies must document ICT risk management and incident reporting. Similar requirements exist for financial entities.

Technical documentation must include software bills of materials (SBOMs). It must also include dependency tracking and secure development evidence. These must be audit-ready documentation sets.

Vibe coding and shadow application development

Alt text: Presentation slide titled “Mitigating the Risk of ‘Shadow Application Development’” illustrating hidden security risks created by generative AI and low-code/no-code tools. The central visual uses an iceberg metaphor: above the waterline are simple app wireframes labeled “Marketing App” and “HR Automation,” representing visible, user-built applications. Below the waterline, a dark, complex mass of interconnected lines, code blocks, databases, and security icons represents hidden technical risk, labeled with issues such as “Exposed API Keys,” “PII Leaks,” and “Unsecured Endpoints.” A warning callout highlights “Hidden Danger: Uncontrolled Risk.” On the right, explanatory text provides context that non-developers building apps create a visibility crisis for security, followed by “The Documentation Fix,” listing guardrails for citizen developers, templates and component libraries, and approved toolchains with security policies. A bold quote at the bottom states, “You cannot secure what you haven’t documented.” Clean, modern enterprise infographic design with white background, orange accents, blueprint-style annotations, and strong focus on documentation, security governance, and risk mitigation.

An increasing amount of code will be developed by AI. And an increasing number of apps will be developed by people who don’t know how to code.

AI-assisted development increases the volume of shipped changes. It also increases system complexity. Both must be understood and operated safely.

Generative AI tools enable non-developers to build applications. Security teams struggle to gain visibility over “shadow” apps. Marketing or HR departments might build these apps.

Low-code/no-code platforms democratise development. However, they still require extensive documentation:

  • Templates and component libraries
  • Best practices for citizen developers
  • Integration guides with traditional code
  • Governance and security policies

Simple automation scripts could expose API keys or personally identifiable information (PII). They require clear documentation of security policies and approved toolchains.

5. The rise of agentic workflows

Alt text: Presentation slide titled “Writing for the ‘Power Reader’ (AI Agents)” explaining how technical documentation must evolve for both human readers and autonomous AI systems. The slide is divided into two horizontal sections. The top section, labeled “Human Consumption (Narrative),” shows an eye icon projecting a cone of vision toward a traditional document page, representing linear, narrative reading by humans. The bottom section, labeled “Agentic Consumption (Structured Metadata),” shows a microchip icon projecting toward a network of interconnected document nodes with tags, schemas, APIs, taxonomies, and JSON-style metadata, illustrating how AI agents consume structured content. On the right, three highlighted panels explain: “The Reality,” stating that AI agents are now power readers that perform tasks and make decisions; “The Requirement,” calling for system instructions, prompt libraries, and componentised content; and “The Risk,” warning that without curated ground truth, AI models hallucinate and require structured data for Retrieval-Augmented Generation (RAG). Clean, modern enterprise infographic design with white background, orange accents, and emphasis on AI agents, structured documentation, metadata, and machine-readable content.

The industry is making greater use of autonomous AI agents. These agents perform tasks, call APIs and make decisions. They’re moving beyond simple chatbots.

Unlike humans, AI agents are “power readers”. They need documentation formatted for machine consumption. This means highly structured “system instructions” and prompt libraries.

Ideally, AI systems use componentised content with metadata and taxonomies. This makes documentation easier for both users and AI to find. Semantic AI within content management systems can understand relationships and enable reuse.

Technical Authors must curate “ground truth” knowledge bases. This prevents AI models from hallucinating. Retrieval-Augmented Generation (RAG) uses metadata to find relevant content and generate answers with citations.

Who should write the documentation?

Different documentation types require different expertise. The table below suggests ownership for various documentation types.

Documentation type Recommended owner
API reference documentation Technical Author with developer
System instructions for AI agents Technical Author with AI engineer
Internal developer platform guides Technical Author
AI technical documentation (Annex IV) Technical Author with compliance team
Software bill of materials (SBOM) Developer with Technical Author
Security recipes and playbooks Technical Author with security engineer
RAG knowledge base curation Technical Author
Code documentation and comments Developer
Incident response playbooks Technical Author with Operations team
Low-code/no-code platform guides Technical Author

Can’t AI write the documentation?

AI can generate some documentation. Generative AI assists with authoring, ensuring consistency and translation.

However, AI produces high volumes of content with variable accuracy. Companies need human-in-the-loop editors. This prevents documentation rot: where stale AI content leads to broken code.

Technical Authors provide content lifecycle management. They audit, prune and “hallucination-check” entire documentation libraries. Human writers transition from sole Authors to editors and information architects. They focus on structure, voice and analytics.

The ROI of documentation

Defensive value

Alt text: Presentation slide titled “The ROI of Documentation: Defensive Value” illustrating how high-quality documentation reduces operational risk in complex systems. The visual features an isometric, blueprint-style diagram of a reinforced structure acting as a barrier against turbulent waves labeled “Risk,” symbolizing system instability. The structure is annotated with technical measurements and labels such as “Structural Integrity,” “Safety,” and “Flow Mitigation,” conveying engineering precision and resilience. On the right side, bullet points highlight key benefits: “Risk Reduction,” noting reduced operational variability and delivery failure rates; “Compliance Insurance,” emphasizing meeting audit requirements such as DORA and the AI Act to avoid fines; and “Support Efficiency,” showing reduced support load and safer onboarding for new engineers. A highlighted takeaway box at the bottom states, “Documentation is an insurance policy against system complexity.” Clean, modern infographic design with white background, orange accents, and strong focus on documentation ROI, risk management, compliance, and enterprise engineering practices.

Documentation reduces operational risk and mitigates support load. It improves onboarding and change safety. It helps organisations satisfy compliance and audit expectations.

Offensive value

Alt text: Presentation slide titled “The ROI of Documentation: Offensive Value” showing how high-quality documentation directly drives growth and revenue for API-first companies. The visual features a glowing orange cable or pipeline rising diagonally upward, labeled from left to right as “Code Foundation” and “Growth Trajectory,” symbolizing increasing business momentum. Inside the pipeline are visible API call examples such as “GET /users/123” and “POST /auth/token” with JSON responses showing `"status": "ok"`, reinforcing the connection between documentation and successful API usage. On the right, bullet points explain key benefits: “Product Surface,” stating that for API-first companies the documentation *is* the product; “Growth Engine,” highlighting direct impact on developer adoption, conversion, and retention; and “Competitive Advantage,” emphasizing optimized “Time to First Call” as a market differentiator. A highlighted takeaway box concludes, “Documentation is a revenue driver.” Clean, blueprint-inspired enterprise design with white background, orange accents, technical annotations, and strong focus on API documentation, developer experience, and business growth.

Documentation is a primary product surface. API and developer documentation directly influences developer adoption, conversion and retention. This is particularly true in API-first and platform business models.

Conclusion

These developments change documentation in three ways:

  • They increase the amount of change that needs documenting
  • They provide new ways to generate and personalise documentation
  • They create governance and trust problems that documentation must solve

As a result, Technical Authors are becoming essential partners in software delivery.

Ready to transform your documentation?

Contact us to learn more about how Cherryleaf can help you with your documentation needs.

Sources

2024 Stack Overflow Developer Survey. Available at: https://survey.stackoverflow.co/2024/

2025 DORA Report: AI Capabilities Model Report. Google Cloud. Available at: https://cloud.google.com/resources/content/2025-dora-ai-capabilities-model-report

AI Act Service Desk – Article 11: Technical documentation. Available at: https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-11

Announcing the 2024 DORA report. Google Cloud Blog. Available at: https://cloud.google.com/blog/products/devops-sre/announcing-the-2024-dora-report

CNCF Annual Survey 2024. Cloud Native Computing Foundation. Available at: https://www.cncf.io/

Software Bill of Materials (SBOM). NIST. Available at: https://www.nist.gov/itl/executive-order-14028-improving-nations-cybersecurity/software-security-supply-chains-software-1

 

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