Agent Frameworks
A practical guide to choosing agent frameworks for building production-ready AI automation systems.
By Eddy Tools Team · Editorial team
Published 1 min read
Agent frameworks provide the scaffolding for building autonomous AI systems—tool registration, memory management, planning loops, and observability hooks. Choosing the right framework depends on your use case complexity, team skills, and deployment environment.
Popular options include LangChain for flexible composition, AutoGen for multi-agent conversations, and Semantic Kernel for enterprise .NET stacks. Each offers different trade-offs in abstraction level, community support, and production readiness.
EddyTools evaluates framework fit for each project rather than defaulting to hype-driven choices. We prototype quickly, measure performance against requirements, and recommend the stack that balances velocity with maintainability.
- Compare frameworks by complexity and production maturity
- Prototype agent workflows before committing to a stack
- Instrument agents for debugging and cost monitoring
Editorial note: Eddy Tools is an AI development and consulting company. Articles are written by the Eddy Tools editorial team and may mention our own services and the tools we build with.
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