decision frameworks

Compare & Decide

Every piece ends with a verdict and the conditions under which it flips. If a comparison can't tell you when to choose the loser, it isn't done.

How to choose an LLM for your agent: Claude, GPT, Gemini, or open-weights

General benchmarks predict agent performance poorly. The six criteria that matter for agentic workloads, a tiering strategy that beats single-model loyalty, and the eval-driven way to decide.

verdict Route by task tier — frontier models for planning, cheaper models for routine steps — and let your own golden-task evals, not leaderboards, pick the names.

The agentic AI tech stack, layer by layer: what you actually need to build agents

Seven layers make up every serious agentic system — models, serving, harness, protocols, knowledge, evals, and controls. What each layer does, the main options in each, and where the choices actually matter.

verdict Pick per layer, keep the seams thin, and spend your innovation budget on tools and evals — the layers nobody can buy for you.

Skills vs. tools vs. hooks vs. subagents vs. prompts: when to use what

Agentic coding assistants offer half a dozen extension mechanisms, and picking wrong wastes context or bites silently. One decision table for instructions, skills, MCP tools, hooks, subagents, and slash commands.

verdict Match the mechanism to the trigger: always-true → instructions file; situational expertise → skill; external action → MCP tool; must-never-be-skipped → hook; big noisy searches → subagent.

Agent frameworks vs. rolling your own harness: build or buy the loop?

LangGraph, CrewAI, the OpenAI and Claude SDKs — or 200 lines of your own code? A decision framework based on what agent frameworks actually provide.

verdict Roll your own for one agent with a handful of tools; adopt a framework when you need durable state, parallel orchestration, or team-wide conventions — and only after building the bare loop once.

MCP servers vs. direct CLI calls: when each wins

Agents can reach tools through an MCP server or by running CLI commands directly. A decision framework with the token economics of each approach.

verdict CLI for exploration and one-offs; MCP once a tool is used often, by many agents, or needs governance.

RAG vs. agent memory vs. fine-tuning: which knowledge problem do you actually have?

Three ways to make a model "know" things, routinely confused with each other. A diagnostic for picking the right one — and the order to try them in.

verdict Retrieval for facts that change, memory for what the system learns in use, fine-tuning for behavior — and almost never for knowledge.

Workflow vs. agent: when BPMN-style orchestration beats dynamic reasoning

The most expensive architecture mistake in enterprise AI is putting an agent where a workflow belongs — or vice versa. A decision framework with a bright-line design principle.

verdict Deterministic paths live in the workflow; dynamic reasoning lives in the agent. Most systems need both, joined at explicit boundaries.