AI & Agent Frameworks
aiBuilding Blocks for LLM-Driven Applications
With the rise of generative AI and LLM-based systems, a new generation of agent frameworks, such as LlamaIndex, LangGraph and many more has emerged to support the development of complex, multi-step, tool-using AI applications.
These frameworks provide building blocks to orchestrate LLMs, tools, memory, and context – enabling everything from simple assistants to autonomous agents.
Why it matters
While basic prompting is easy, building maintainable and extensible AI applications that met quality expectations is not trivial. Agent frameworks help:
- Structure reasoning and decision-making
- Integrate external tools (APIs, databases, functions)
- Manage memory and context windows
- Enable traceability, observability, and evaluation
Conclusion and Further Reading
Agent frameworks are essential enablers for moving from experiments to structured, reusable AI components. Choosing the right framework impacts scalability, maintainability, and team productivity.
For detailed comparisons, patterns, and recommendations, visit the AOE AI Radar.