Honest decision guides for the AI architecture choices that matter — the meta-framework, agent frameworks, model platforms, RAG vs fine-tuning vs long-context, MCP vs A2A, managed vs self-hosted, and vector storage — framed by durable trade-offs, not hype.
Most AI architecture debates are settled by hype, familiarity, or whoever spoke last — this series settles them by requirements and trade-offs, starting with the meta-framework that every specific decision reduces to.
Most AI architecture debates are settled by hype or familiarity; this series settles them by requirements and trade-offs, starting with the meta-framework every specific decision reduces to.
Four popular agent frameworks, four genuinely different philosophies — and the right choice is decided less by features than by how much control you want, how your team thinks, and what you're actually building.
Four popular agent frameworks, four genuinely different philosophies — the right choice is decided less by features than by how much control you want, how your team thinks, and what you're building.
The model platform decision is usually decided before you compare models at all — by which cloud you're already on, what governance you need, and whether you're renting inference or running it — and getting that framing right matters more than any benchmark.
The model-platform decision is usually settled before you compare models — by which cloud you're on, what governance you need, and whether you're renting inference or running it.
The most common architecture mistake in applied AI is reaching for fine-tuning to fix a knowledge problem — so the single most useful rule here is that RAG is for knowledge and fine-tuning is for behavior, and long-context is a convenience, not a strategy.
The most common architecture mistake is reaching for fine-tuning to fix a knowledge problem — so the key rule: RAG is for knowledge, fine-tuning is for behavior, and long-context is a convenience, not a strategy.
The most common question about the two big agent protocols is which one to use — and the answer is almost always "both," because they solve different problems: MCP connects an agent to its tools, A2A connects an agent to other agents.
The most common question about the two big agent protocols is which to use — and the answer is almost always both, because MCP connects an agent to its tools and A2A connects an agent to other agents.
This is the classic fixed-versus-marginal decision, and it has a clean answer: managed APIs win until your volume is high and steady enough to keep expensive GPUs busy — which is a much higher bar than most teams assume.
The classic fixed-vs-marginal decision with a clean answer: managed APIs win until your volume is high and steady enough to keep expensive GPUs busy — a much higher bar than most teams assume.
The vector-storage decision has a boringly practical answer that cuts against the hype: for most systems, the database you already run with a vector extension beats adding a new specialized system — until scale or specific features force the upgrade.
A boringly practical answer that cuts against the hype: for most systems the database you already run with a vector extension beats adding a specialized system — until scale or specific features force the upgrade.
This series is part of a larger body of work by Pratik Dhanave, an Agentic AI Architect writing about production AI systems, distributed systems, and cloud-native engineering. Explore all course series, browse every post, or find topics via the tag index.