AI Crosses from Exploration into Engineering
November 11, 2025
The Thoughtworks Technology Radar has always been more than a list of tools. It captures what happens when technology moves from experimentation to everyday practice. In its 33rd edition, one shift stands out: AI has crossed from exploration into engineering.
Developing with Agents
Earlier Radar editions focused on Generative AI as a productivity booster. Now, AI shows up as an architectural force. The emergence of agents, powered by the Model Context Protocol (MCP), points to a world where software components reason, negotiate, and collaborate — not just execute.
The newest radar entries illustrate this change:
- Context Engineering — Designing what an AI knows and how it interprets its world.
- Agent-to-Agent (A2A) Protocol — Allowing autonomous agents to coordinate directly.
- AG-UI Protocol — Connecting agents to interfaces and user experiences.
- AGENTS.md — A simple way to describe an agent’s purpose, scope, and interface.
- Anchoring coding agents to a reference application — Grounding agents in a real, trusted codebase.
Together, they signal a maturing ecosystem of connected AI systems — where context, not code, becomes the main design material.
AI Coding Workflows
The Radar also documents how AI is reshaping software delivery workflows. It’s no longer about asking a copilot to complete a line of code. Instead, teams are learning how to engineer entire flows that blend human insight with machine assistance.
Among the new techniques:
- Spec-driven development — Define the intent before the implementation.
- Using GenAI to understand legacy codebases — Making modernization less painful and more informed.
- GenAI for forward engineering — Translating business concepts into architecture proposals.
- Curated shared instructions for software teams — Establishing collective knowledge for AI-assisted work.
- Self-serve UI prototyping with GenAI — Letting teams explore product ideas through conversation.
- Context7 — Treating context as a first-class engineering concern.
This is AI entering the SDLC — not as an afterthought, but as part of design, discovery, and delivery.
New AI Antipatterns
As always, the Radar balances enthusiasm with realism. Several AI-related antipatterns warn of the new kinds of technical debt emerging:
- Complacency with AI-generated code — When speed overshadows quality.
- AI-accelerated Shadow IT — Local scripts and agents outside governance.
- Naive API-to-MCP conversion — Wrapping legacy APIs without redesign.
- Text-to-SQL — Giving models too much direct power over data.
Each of these reflects a familiar lesson: AI doesn’t remove the need for good engineering — it magnifies both discipline and disorder.
Conclusion
Radar 33 captures a turning point: AI is no longer a lab experiment or a productivity add-on — it’s becoming a core capability of modern software engineering. We are learning not just to use AI, but to engineer with it.