Most AI applications today follow a simple pattern: input goes in, one model processes it, output comes out. But real-world problems need more than that. They need multiple agents working together, sharing context, and adapting to changing conditions.

This is where agent orchestration comes in. Instead of rigid chains, we’re building dynamic systems where agents can collaborate, route information, and even fix themselves when things go wrong.

The Problem with Sequential Pipelines

LangChain made it easy to chain prompts together. You take output from one model and feed it to the next. It works for simple tasks, but breaks down when you need:

• Multiple agents working on different parts of a problem
• Dynamic routing based on context
• Error recovery and self-correction
• Real-time adaptation to changing conditions

Sequential pipelines are like assembly lines. If one step fails, everything stops. If you need to change the order, you rebuild the whole thing. And if you want agents to share information or work in parallel, you’re out of luck.

Event-Driven Orchestration

The first pattern that changes this is event-driven orchestration. Instead of following a fixed sequence, agents react to events and signals.

Here’s how it works:

User Request → Event Bus → Multiple Agents Listen
                     ↓
              Agents publish results
                     ↓
              Other agents react to results

Each agent subscribes to events it cares about. When something happens, relevant agents wake up and do their work. This creates a more flexible system where agents can collaborate naturally.

Blackboard Architecture

Another pattern is the blackboard architecture. Think of it as a shared workspace where agents can read and write information.

Agent A writes: "User wants to book a flight"
Agent B reads this, adds: "Found 3 flight options"
Agent C reads both, writes: "Best option is Flight 123"

Agents work asynchronously, building on each other’s work. No one agent controls the flow. The blackboard becomes the coordination mechanism.

Self-Healing Workflows

The most interesting pattern is self-healing workflows. These systems can detect problems and fix them automatically.

For example:

1. An agent fails to get a response from an API
2. The system detects the failure
3. It routes the request to a backup agent
4. If that fails too, it tries a different approach
5. The workflow continues without human intervention

Code Example: Dynamic Agent Graph

Let me show you how this looks in practice. Here’s a simple implementation using LangGraph:

from langgraph.graph import StateGraph, END
from typing import TypedDict, List
import json

class AgentState(TypedDict):
    user_input: str
    context: dict
    results: List[dict]
    current_agent: str

class RouterAgent:
    def __init__(self):
        self.name = "router"
    
    def route(self, state: AgentState) -> str:
        """Route to appropriate specialist based on input"""
        input_text = state["user_input"].lower()
        
        if "research" in input_text or "find" in input_text:
            return "research_agent"
        elif "summarize" in input_text or "summary" in input_text:
            return "summarizer_agent"
        else:
            return "general_agent"

class ResearchAgent:
    def __init__(self):
        self.name = "research_agent"
    
    def research(self, state: AgentState) -> AgentState:
        """Simulate research work"""
        # In real implementation, this would call APIs, search databases, etc.
        result = {
            "agent": self.name,
            "action": "research",
            "data": f"Research results for: {state['user_input']}",
            "confidence": 0.85
        }
        
        state["results"].append(result)
        state["current_agent"] = self.name
        return state

class SummarizerAgent:
    def __init__(self):
        self.name = "summarizer_agent"
    
    def summarize(self, state: AgentState) -> AgentState:
        """Summarize previous results"""
        previous_results = state["results"]
        summary = f"Summary of {len(previous_results)} research findings"
        
        result = {
            "agent": self.name,
            "action": "summarize",
            "data": summary,
            "confidence": 0.90
        }
        
        state["results"].append(result)
        state["current_agent"] = self.name
        return state

# Build the graph
def build_agent_graph():
    workflow = StateGraph(AgentState)
    
    # Add nodes
    router = RouterAgent()
    research_agent = ResearchAgent()
    summarizer = SummarizerAgent()
    
    workflow.add_node("router", router.route)
    workflow.add_node("research", research_agent.research)
    workflow.add_node("summarize", summarizer.summarize)
    
    # Add edges
    workflow.add_edge("router", "research")
    workflow.add_edge("router", "summarize")
    workflow.add_edge("research", END)
    workflow.add_edge("summarize", END)
    
    # Set entry point
    workflow.set_entry_point("router")
    
    return workflow.compile()

# Example usage
if __name__ == "__main__":
    graph = build_agent_graph()
    
    initial_state = {
        "user_input": "Research the latest AI trends",
        "context": {},
        "results": [],
        "current_agent": ""
    }
    
    result = graph.invoke(initial_state)
    print(json.dumps(result, indent=2))

JSON-Based Agent Graph Definition

You can also define agent workflows using JSON configuration:

{
  "workflow": {
    "name": "research_and_summarize",
    "version": "1.0",
    "agents": {
      "router": {
        "type": "router",
        "rules": [
          {
            "condition": "input contains 'research'",
            "route_to": "research_agent"
          },
          {
            "condition": "input contains 'summarize'",
            "route_to": "summarizer_agent"
          }
        ]
      },
      "research_agent": {
        "type": "specialist",
        "capabilities": ["web_search", "data_analysis"],
        "timeout": 30,
        "retry_count": 3
      },
      "summarizer_agent": {
        "type": "specialist",
        "capabilities": ["text_summarization", "report_generation"],
        "depends_on": ["research_agent"]
      }
    },
    "routing": {
      "strategy": "event_driven",
      "fallback": "general_agent"
    },
    "monitoring": {
      "enabled": true,
      "metrics": ["latency", "success_rate", "error_count"]
    }
  }
}

Memory Management

When agents work together, they need to share context efficiently. Here are some approaches:

Vector Stores: Store embeddings of previous conversations and results. Agents can search for relevant context.

Episodic Memory: Keep track of what happened in previous interactions. Useful for maintaining conversation flow.

Shared State: Use a central store that all agents can read and write to. Make sure to handle concurrent access properly.

Observability and Testing

Multi-agent systems are complex. You need good monitoring to understand what’s happening:

Logging: Track which agents are called, how long they take, and what they produce.

Metrics: Monitor success rates, latency, and resource usage across the system.

Testing: Create test scenarios that simulate different conditions and edge cases.

The Future of Agent Orchestration

We’re moving from simple prompt chaining to sophisticated cognitive systems. The next steps include:

• Runtime Governance: Systems that can modify their own behavior based on performance
• AgentOps Dashboards: Real-time monitoring and control of agent workflows
• Adaptive Learning: Agents that improve their coordination over time

The goal isn’t just to make agents work together. It’s to create systems that can think, adapt, and solve problems in ways that single models never could.

Conclusion

Agent orchestration patterns give us tools to build more intelligent, flexible systems. Event-driven workflows, blackboard architectures, and self-healing mechanisms let us move beyond rigid pipelines.

The code examples show how to implement these patterns today. The JSON configuration approach makes it easy to modify workflows without changing code.

As these patterns mature, we’ll see more sophisticated agent systems that can handle complex, real-world problems. The future of AI isn’t just better models—it’s better ways to coordinate them.