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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.
A practical playbook for keeping agent knowledge current with source SLAs, expiration rules, and retrieval pipelines that age gracefully.
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Access GPT-4 and other powerful AI models for your agent development.
Advanced framework for building applications with large language models.
High-performance vector database for AI applications and semantic search.
Complete course on building production-ready AI agents from scratch.
Start with the free tiers of these tools to experiment, then upgrade as your AI agent projects grow. Most successful developers use a combination of 2-3 core tools rather than trying everything at once.
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.
A practical cost-control playbook for AI agents, focused on predictable spend without gutting quality.
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