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Move beyond feature matrices. A practical guide to selecting an AI agent platform through use-case fit, controlled pilots, and measurable exit criteria.
A practical architecture for agents that can show where an answer came from, how current it is, and when a human should verify it.
More reasoning is not always better. This guide explains how to give agents adaptive thinking budgets based on risk, uncertainty, cost, and workflow value.
New Interactive Tools
Explore six decision tools that recommend solution types, estimate complexity, and point people toward the right articles for their use case.
Solution Type Quiz
Decide between automation, RAG, copilots, and agents.
Readiness Scorecard
Spot weak foundations before you commit to a build.
Context Decision Tool
Figure out when the agent should search, remember, or ask.
Prompt engineering is too small for production agents. Context engineering is the discipline of deciding what the agent should see, when it should see it, and what must stay out.
Benchmarks are useful, but they do not tell you whether an agent will survive your workflow. Production evals need traces, tools, permissions, edge cases, and human review.
Chat is a useful command layer, but many agent workflows need tables, timelines, approvals, previews, and dashboards. This guide explains how to design agent UX beyond the text box.
Computer-use agents are becoming useful, but giving them a normal desktop is reckless. This guide explains how sandboxes, policies, and action gates make computer-use agents safer.
Most agent dashboards count chats, tokens, and thumbs-up reactions. Product teams need outcome analytics: resolved tasks, avoided work, user trust, escalation quality, and retained value.
An updated 2026 comparison of the top AI agent frameworks, including LangGraph, OpenAI Agents SDK, Microsoft Agent Framework, Google ADK, CrewAI, LlamaIndex, Pydantic AI, Mastra, Agno, and Claude Agent SDK.