How to choose an AI agent framework for a real workflow
Turn a shortlist of agent frameworks into a practical decision using task boundaries, recovery tests, integration needs and maintenance costs.
Read the guideDECISIONS BEFORE DEPENDENCIES
A little clarity.
A better next build.
Practical guides to choosing and operating AI tools. Clear questions, concrete steps and sources you can follow.
An MCP server checklist before you connect real accounts
Review an MCP server's identity, exposed tools, account permissions, deployment model and failure behavior before using production credentials.
Read the guideAgent memory vs RAG: decide what should be remembered
Separate task state, user preferences and source documents, then choose storage, retrieval and deletion rules for each type of information.
Read the guideHow to evaluate an AI agent before a production rollout
Create an agent evaluation around observable outcomes, forbidden actions, realistic failure cases and the cost of completing useful work.
Read the guideBrowser agent safety: design the boundary before the click
Define account access, permitted destinations, action approvals and recovery evidence for a browser agent before allowing it to change real systems.
Read the guideSelf-hosted AI agents: calculate cost per completed task
Build a practical cost worksheet for self-hosted agents that includes models, tools, retries, idle infrastructure and human review.
Read the guide