An AI agentic framework is a software development toolkit or runtime environment designed to make it easier to build, run, and orchestrate AI agents. These frameworks provide the underlying architecture, libraries, and abstractions that developers use to implement agent capabilities such as reasoning, planning, tool use, memory, and multi-agent coordination.

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Purpose:

AI agentic frameworks aim to simplify the creation of complex AI agents by offering ready-made components, integrations, and design patterns. They help teams focus on defining agent behavior and workflows rather than building foundational infrastructure from scratch.

Key Characteristics:

• Agent Lifecycle Management — Provides mechanisms for creating, running, pausing, and terminating agents.
• Reasoning & Planning Modules — Supports task decomposition, decision-making, and execution planning.
• Tool & API Integration — Offers built-in connectors or adapters for external services, data sources, and protocols.
• State & Memory Handling — Allows agents to store, retrieve, and update context over time.
• Multi-Agent Orchestration — Facilitates communication and collaboration between multiple agents within a system.

Use Cases:

• Developing autonomous research assistants that can search, summarize, and generate reports.
• Orchestrating multiple specialized agents to work together on complex business processes.
• Integrating AI agents with enterprise applications and workflows through prebuilt connectors.

Security Considerations:

Agentic frameworks should be paired with protocol-level governance (e.g., MCP, A2A) to ensure interoperability and secure communication across systems. Developers should also implement least-privilege permissions, input/output validation, and auditing to mitigate misuse.

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Example in Practice:

A development team might use an agentic framework like LangChain, CrewAI, or Microsoft Autogen to create a project management AI assistant that coordinates with other agents, updates tasks in Jira, and generates status reports — all using the framework’s built-in orchestration tools.

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Related terms:

• AI Agent Protocol – A standardized method for AI agents to securely communicate, negotiate, and exchange data with other agents, tools, or services.
• Model Context Protocol (MCP) – A protocol for connecting AI agents to external tools, data sources, and applications.
• Agent-to-Agent Protocol (A2A) – A protocol for secure, interoperable agent-to-agent communication.