22 total posts in this topic.
Google's Gemini Spark and Antigravity 2.0 represent an inflection point for businesses building AI agents. Here's the decision framework for when to use off-the-shelf agents, when to build on a platform, and when custom architecture is still the only real option.
80% of AI projects fail to deliver business value, and choosing the wrong development partner is the fastest path there. Here are the 7 questions that reveal whether an AI firm builds production systems or just impressive demos.
Princeton NLP found single agents match or outperform multi-agent systems on 64% of tasks. Here's the decision framework for when multi-agent orchestration is worth the complexity - and when a well-built single agent is the smarter investment.
Reasoning models can show their thinking - but research shows up to 40% of that thinking is performative. Here's what actually constitutes an accountable AI agent architecture, and what to build before the EU AI Act deadline hits.
73% of successful enterprise AI deployments started deliberately small. Here's the scoping framework that separates pilots that scale from expensive science projects - with research-backed criteria for what to build first, how to measure success, and when to expand.
Retainer, fixed-scope sprint, or embedded team? Here's how to choose the right AI consulting engagement model for your situation - before you commit to anything.
AI coding agents can double your output - but if they also double your maintenance burden, you've quadrupled your costs. Here's the math, the research, and how to avoid the trap.
WebRTC and WebSocket both work for AI voice agents, but choosing wrong adds months of rework. Here's the architectural decision framework - with latency math, production tradeoffs, and clear recommendations for each use case.
Not every AI agent needs full autonomy. Here's a practical framework for choosing the right autonomy level for each task - with real-world examples of what happens when you get it wrong.
