Empowering autonomous workflows with agents that think, act, and adapt.
1. User/Client Input (External Trigger)
Initiates the workflow (e.g., a user request, system event, or external API call).
Acts as the starting point for autonomous task generation.
2. Orchestrator / Controller
Manages overall workflow logic
Delegates tasks to the right agents.
Determines sequence and communication between components.
3. Agent Layer (Agent 1, Agent 2, Agent 3...)
Specialized agents assigned specific responsibilities.
Agents may be designed for:
Scheduling
Data processing
Communication
Analysis or summarization
4. Reasoning Layer / LLM
Handles logic, decision-making, and exception management.
Provides cognitive ability for agents (reasoning, inference, language understanding).
Summary: Agentic AI
It introduces a new paradigm in automation by orchestrating autonomous agents using LLMs, memory, and external tools. This layered architecture dynamically generates, executes, and adjusts workflows with minimal human intervention. It leverages orchestration engines, specialized agents, memory stores, APIs, and feedback loops to operate complex tasks autonomously.
Agentic AI
Memory & Tool Access Layer
- Memory: Redis, Pinecone — stores conversation or workflow state, context, or user preferences.
- APIs/Tools: Integration with enterprise systems like HRMS, calendars, CRMs, etc.
Feedback & Logging
- Collects real-time data on workflow performance.
- Helps in monitoring, improving, and adjusting tasks or agent behavior.
- Enables self-healing and adaptive learning loops.
Key Capabilities Enabled by Agentic AI
- Multi-agent collaboration for complex task decomposition
- Autonomous decision-making and recovery from exceptions
- Dynamic interaction with APIs and data stores
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