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The SDLC is breaking under the weight of AI agents. You can't build non-deterministic systems with deterministic methodologies. We need "Agile for Agents" - the ADLC. Most teams are still trying to QA their way out of probabilistic behavior.

The four levels of agentic systems L1: Assistive agents L2: Knowledge agents L3: Action agents L4: Multi-agent system

The biggest mistake in enterprise AI is building autonomous agents when you need simple automation. Most problems get solved at level 1-2 autonomy. Stop building reasoning agents for deterministic workflows.

The future of enterprise AI isn't only developer-centric. Process owners need to be in the driver's seat from day one. They know the workflows, decision logic, and what "good" actually looks like. Devs become enablers, not only architects.

Agent QA isn't just "did it break?" but "can we trust it?" You're not testing for just correctness anymore - you're evaluating behavioral confidence. Intent, escalation logic, fallback paths. Traditional testing frameworks miss this entirely.

Git doesn't work for agents. Same prompt + different model weights = completely different behavior. You need to version intelligence, not just code. Prompts, tool schemas, execution traces, reasoning steps. We're still using 2005 tools for 2025 problems.

“There are no AI experts, only people who have practiced more than you” - Nathaniel Whittemore

Biggest lesson I’ve learned from working in AI product + DevRel/DX in the last 14 months is that curveballs are a feature, not a bug. The market moves fast, competition is fierce. Adapt or die.

Maybe the real Windsurf was the friends we made along the way