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Too many teams reach for retrieval before they understand the real problem. This practitioner-focused guide explains when an AI agent should search, when it should rely on memory, and when it should stop and ask a clarifying question instead.
A practical architecture pattern for using smaller models to act and stronger models to verify, keeping agent quality high without paying premium cost on every step.
A practical guide to running agents in shadow mode so you can compare outputs, measure risk, and launch with fewer surprises.
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.
A practical playbook for keeping agent knowledge current with source SLAs, expiration rules, and retrieval pipelines that age gracefully.
A practical guide to designing background agents that work through queues, recover gracefully, and avoid turning every workflow into a chat session.
A practical security architecture for giving AI agents scoped access through identity layers, short-lived credentials, and brokered permissions.
A practical guide to keeping agent memory useful, safe, and inexpensive by design.
A practical guide to designing clean handoffs so humans stay in control without slowing your agent down.
A practical guide to designing intake forms and request schemas so your AI agent stops guessing and starts delivering consistent outcomes.
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