We are deliberately unsentimental about it. An agent is a component with a non-deterministic output, and it needs the same discipline as any other component: a defined contract, a way to measure quality, a rollback path and an owner. If a workflow cannot be described as rules, a model will not rescue it. If a model cannot be evaluated, it is not ready for production.
The library is smaller here because we only publish when we have something specific to say. Coverage grows as we complete the 2026–27 research, including the EU AI Act transparency deadline and the cleanup work that follows AI-generated code.
For the commercial side, the AI engineering and automation service is where this becomes an engagement. For the decision itself, start with agents versus traditional automation.
All articles in this topic
- 01 AI Agents vs Traditional Automation: When to Use Each AI agents and traditional automation serve different purposes. Learn when to use each approach and how to combine them for maximum impact. 9 min read Published Read
- 02 The AI Production Gap: Why Pilots Fail Most AI experiments never become production systems. Learn the common failure patterns and how to bridge the gap from proof of concept to reliable production. 10 min read Published Read
- 03 Technology Consulting Trends Shaping 2025 AI-augmented development, platform engineering, cloud cost optimization, and more. Learn the technology trends that will impact your business in 2025. 10 min read Published Read
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