Agent Framework

An agent framework provides the runtime infrastructure that makes it practical to build production AI agents without reinventing common patterns from scratch. Core capabilities that frameworks provide include: the execution loop that drives the agent through plan-act-observe cycles; tool registration and invocation, including handling structured tool call formats and routing results back to the model; conversation and working memory management; error handling and retry logic for failed tool calls; observability and logging of agent steps for debugging; and in more advanced frameworks, multi-agent coordination where agents can spawn sub-agents or coordinate with parallel workers. Without a framework, each of these concerns requires custom implementation — manageable for simple agents but quickly becomes architectural complexity that buries the actual business logic of the application.

Leading agent frameworks each reflect different architectural philosophies. LangGraph (from LangChain) models agents as explicit state machines — defining states, transitions, and conditional routing that makes complex agent workflows easier to visualize and debug. The Anthropic Claude agent SDK provides native integration with Claude's tool use capabilities and streaming. AutoGen and CrewAI focus on multi-agent patterns where multiple AI actors collaborate. Semantic Kernel targets enterprise .NET and Python developers building AI into existing software stacks. The right choice depends on: the complexity of the orchestration logic (simple linear pipelines vs. complex branching state machines), the team's existing technology stack, the observability and debugging requirements, and whether multi-agent coordination is a core need.

For B2B teams building AI applications, adopting a mature agent framework versus building custom orchestration is typically a clear decision: frameworks exist because the underlying problems (state management, error handling, observability, tool integration) are genuinely hard and teams underestimate them. The cost of adopting a framework is learning its abstractions and constraints; the cost of not adopting one is rebuilding those same abstractions, poorly, under production pressure. As agent applications grow more complex — adding more tools, more agents, more conditional logic — the value of a well-designed framework's structure compounds. The practical guidance: start with a framework for any agent that will run in production, and only consider custom infrastructure if the framework's constraints genuinely block something the application requires.