“Generative AI is becoming as essential to the software development process as IDEs, testing, and DevOps automation tools. Engineers are learning the best uses of reasoning LLMs across a growing number of agentic, multi-step tasks. MCP servers extend the capabilities of development and automation tools vendors so they are now accessible by LLMs used in development.”

Mitch Ashley

Vice President & Practice Lead, CIO & Technology Buyers, and Software Lifecycle Engineering

Developers Engage AI to Augment Work

By the end of 2025, over 70% of software developers will use generative AI to augment development work, including code generation, completion, review, analysis, bug fixing, or unit tests. Of developers and DevOps engineers, 20% will use AI and open standards to automate tasks and basic workflows as the use of agentic AI expands to platform engineering.

  • AI Within CLIs and IDEs: This brings generative AI directly into the workflows of developers who frequently work across multiple interfaces to perform development work, configuration, and automation.
  • Reasoning-Capable LLMs: These are incrementally capable of taking on and agentically performing core multi-step development tasks, including code pull requests, environment setup, execution of unit tests, code translation, and documentation.
  • Larger Context Windows and Reasoning LLMs: These enable the understanding of codebases and technical documentation and aid developers in working across longer conversation windows and tasks.
  • Vendor MCP Servers: These servers, accessible via development tools, extend access to the services and tools across development and automation tools used by developers and platform engineers.
  • Agentic AI Automation of Development Tasks: These tasks include analyzing and validating requirements and product definition documents, code refactoring across multiple files, creating unit and some integration-level testing, and performing root cause analysis.
  • Automate and Monitor DevOps Tool Chains: Analyze log files and telemetry data across multiple tools and platforms to automate and monitor DevOps tool chains and critical continuous integration/continuous deployment (CI/CD) processes.
  • Analyze Full Codebases: This process identifies complex interdependencies between code, libraries, packages, and images.
  • Follow the Steps of a Code Plan: Apply multistep reasoning to ensure the proper execution of steps, effective error handling, and appropriate human intervention in cases of errors or unexpected results.
  • Treating Prompts as Code: Both prompts written by engineers and meta-prompts created by LLMs are treated as code to ensure they are captured, tracked, tested, version-controlled, and dependencies managed as these become an integral part of software development processes.

Mitch Ashley is VP and Practice Lead for the CIO & Technology Buyers and Software Lifecycle Engineering practices at The Futurum Group. A multi-time CIO and CTO with 30+ years leading technical organizations, Mitch built and operated production systems spanning cybersecurity for the U.S. Department of Defense, PKI services for the broadband and 5G industries, SaaS platforms, large-scale telecom and banking systems, and a national broadband network. His work with AI began early, developing expert systems that diagnosed and repaired complex mainframe environments. That operator foundation grounds his analysis in operational consequence, covering the technology buyer’s world of software engineering, cybersecurity, DevOps, cloud, and AI.

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The vision behind everything in Futurum’s Custom Research practice is this: research should show you what is happening, what comes next, and what to do about it. It should be personal to each audience, easy for people to grasp, and structured so LLMs can reason over it accurately. And it should be fast and turnkey; you want answers now, not another project to carry for quarters.

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Benjamin Brown, VP Custom Research, Futurum Research

Benjamin Brown

VP, Custom Research · The Futurum Group

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