The generative AI tsunami—kickstarted by OpenAI’s ChatGPT—made AI the centerpiece of technological discourse. ChatGPT’s early experience felt almost magical, but since then, numerous competitors have emerged: Anthropic’s Claude, Google’s Gemini, Meta’s Llama, and Amazon’s newly unveiled Nova.
Hallucinations remain a challenge—but they’re context-dependent. Creativity is a feature for some use cases; for others (e.g., financial analysis), it’s a dealbreaker.
Smaller models trained on targeted data sets might outperform general-purpose giants for niche tasks.
Agents are powerful but still unpredictable. Engineering teams need to assess risk and design APIs with appropriate guardrails. While APIs are traditionally designed for human operators, agentic systems introduce new usage patterns that require proactive consideration.
Documenting APIs—so agents can interact with them effectively—will be key to ensuring safe, efficient usage.
The rise of agentic AI marks a pivotal shift in how software systems will be built and operated. This wave will highlight a truth that many engineering leaders are just beginning to realize: the power to deliver AI solutions lies in their APIs.