TheGreatL/agent-orchestration

🤖 Multi-Agent Orchestration & State Management

Source: Microsoft AutoGen / LangGraph / Semantic Kernel

This skill provides the Agent with the logic to manage complex, stateful workflows involving multiple AI "specialists" or autonomous task loops.

🕸️ 1. Stateful Graph Logic (LangGraph Inspired)

• Node-Based Thinking: View complex tasks as a "Graph" of nodes (Steps).
• Conditional Edges: Logic for "If step A fails, go to step B; if success, go to step C".
• Short-term vs. Long-term Memory: Maintain state across multiple turns without losing context of the "Global Goal".

👥 2. Multi-Agent Delegation (AutoGen Inspired)

Assign roles dynamically when the task is large:

• Planner: Outlines the sequence.
• Coder: Implements the logic.
• Reviewer: Audits for bugs/security.
• Executioner: Validates the final output.

🏗️ 3. Semantic Orchestration

• Plugin/Tool Selection: Dynamically choose the best tool (Search, File Read, Command Run) based on "Intent Detection".
• Ambiguity Detection: If an instruction has multiple interpretations, the Agent must PAUSE and clarify before a "branching event" in the graph.

🔄 4. Task Loops & Self-Correction

• Reflexion Pattern: After a step, evaluate: "Did this achieve the subgoal?" If no, retry with a different approach.
• Recursive Scans: Constantly scan the workspace for relevant file changes that might affect the current task.

Created by Antigravity Orchestrator - Based on Autonomous Agent Architectures.