AI Agent Orchestrator Development (Agent Orchestration)

Imagine you have five AI agents. Each has its own expertise — analyzing finances, checking legal documents, assessing the market. You command a company due diligence. Without an orchestrator, chaos begins: agents are called sequentially, errors restart everything, parallel tasks are missing, and p99 latency exceeds a minute. We encountered this on a real project and built an orchestrator with LangGraph. It cut DD execution time from 4 weeks to 3 days. The orchestrator is 9 times faster. Aspect coverage reaches 94%. This is not the limit — even greater efficiency is possible.

Why are agents ineffective without an orchestrator?

A typical problem: each agent works in isolation. You pass a request from researcher to analyst to writer. An error at any stage restarts the entire chain. If one agent hangs, the whole process stalls. Parallel tasks are absent. The result is low GPU utilization (about 15%), huge latency, and constant manual fixes. Manual due diligence takes up to 4 weeks. For a large project, costs can exceed $100,000.

How does LangGraph simplify orchestration?

LangGraph is a graph-based framework. It describes dependencies between agents as a directed graph. Nodes are agents. Edges are conditional transitions. Built-in checkpoints via MemorySaver save state after each step. Retry logic and fallback are out-of-the-box. Below is an implementation of an orchestrator with LangGraph that we use in commercial projects.

from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver
from typing import TypedDict, Annotated
from langchain_openai import ChatOpenAI
import operator
import json

class OrchestratorState(TypedDict):
    user_request: str
    task_plan: list[dict]           # [{task_id, description, agent, status, result}]
    current_task_index: int
    agent_results: Annotated[dict, lambda a, b: {**a, **b}]
    final_response: str
    error_count: int

llm = ChatOpenAI(model="gpt-4o", temperature=0)

# Agent registry
AGENT_REGISTRY = {
    "researcher": ResearcherAgent(),
    "analyst": AnalystAgent(),
    "writer": WriterAgent(),
    "sql_agent": SQLAgent(),
    "code_interpreter": CodeInterpreterAgent(),
    "file_processor": FileProcessorAgent(),
}

def plan_tasks(state: OrchestratorState) -> OrchestratorState:
    """Orchestrator breaks down task into subtasks and assigns agents"""
    available_agents = list(AGENT_REGISTRY.keys())
    response = llm.invoke(f"""
Break down the following task into subtasks and assign an agent for each.
Available agents: {available_agents}

Task: {state["user_request"]}

Return a JSON list:
[{{"task_id": "t1", "description": "...", "agent": "researcher", "dependencies": []}}]
Dependencies: list of task_ids that must be completed before this task.
""")
    task_plan = json.loads(response.content)
    for task in task_plan:
        task["status"] = "pending"
        task["result"] = None
    return {**state, "task_plan": task_plan, "current_task_index": 0}

def execute_next_task(state: OrchestratorState) -> OrchestratorState:
    """Executes the next ready task"""
    task_plan = state["task_plan"].copy()
    # Find the next task whose all dependencies are completed
    next_task = None
    for task in task_plan:
        if task["status"] == "pending":
            deps_completed = all(
                any(t["task_id"] == dep and t["status"] == "completed"
                    for t in task_plan)
                for dep in task.get("dependencies", [])
            )
            if deps_completed:
                next_task = task
                break
    if not next_task:
        return {**state, "current_task_index": -1}  # All tasks completed
    # Execute task via corresponding agent
    agent = AGENT_REGISTRY.get(next_task["agent"])
    if not agent:
        next_task["status"] = "failed"
        next_task["result"] = f"Agent {next_task['agent']} not found"
    else:
        dependency_results = {
            dep: state["agent_results"].get(dep)
            for dep in next_task.get("dependencies", [])
        }
        try:
            result = agent.execute(
                task=next_task["description"],
                context=dependency_results,
            )
            next_task["status"] = "completed"
            next_task["result"] = result
        except Exception as e:
            next_task["status"] = "failed"
            next_task["result"] = str(e)
    # Update plan
    updated_plan = [
        task if task["task_id"] != next_task["task_id"] else next_task
        for task in task_plan
    ]
    return {
        **state,
        "task_plan": updated_plan,
        "agent_results": {next_task["task_id"]: next_task["result"]},
    }

def should_continue(state: OrchestratorState) -> str:
    """Determines the next step of the orchestrator"""
    pending = [t for t in state["task_plan"] if t["status"] == "pending"]
    failed = [t for t in state["task_plan"] if t["status"] == "failed"]
    if failed and state["error_count"] >= 3:
        return "finalize_with_errors"
    if not pending:
        return "aggregate_results"
    return "execute_next"

def aggregate_results(state: OrchestratorState) -> OrchestratorState:
    """Aggregates all agent results into a final response"""
    all_results = {t["task_id"]: t["result"] for t in state["task_plan"]}
    final = llm.invoke(f"""
Based on the results from different agents, form a final response.
Original request: {state["user_request"]}
Results: {json.dumps(all_results, ensure_ascii=False)}
""").content
    return {**state, "final_response": final}

# Build graph
graph = StateGraph(OrchestratorState)
graph.add_node("plan", plan_tasks)
graph.add_node("execute_next", execute_next_task)
graph.add_node("aggregate_results", aggregate_results)
graph.set_entry_point("plan")
graph.add_edge("plan", "execute_next")
graph.add_conditional_edges("execute_next", should_continue, {
    "execute_next": "execute_next",
    "aggregate_results": "aggregate_results",
    "finalize_with_errors": "aggregate_results",
})
graph.add_edge("aggregate_results", END)
orchestrator = graph.compile(checkpointer=MemorySaver())

How does the orchestrator execute tasks in parallel?

import asyncio

async def execute_parallel_tasks(tasks_batch: list[dict]) -> list[dict]:
    """Parallel execution of independent tasks"""
    coroutines = []
    for task in tasks_batch:
        agent = AGENT_REGISTRY.get(task["agent"])
        if agent:
            coroutines.append(asyncio.to_thread(agent.execute, task=task["description"]))
    results = await asyncio.gather(*coroutines, return_exceptions=True)
    for task, result in zip(tasks_batch, results):
        if isinstance(result, Exception):
            task["status"] = "failed"
            task["result"] = str(result)
        else:
            task["status"] = "completed"
            task["result"] = result
    return tasks_batch

Practical case: orchestrator for due diligence

From our practice: automated company vetting for M&A. Parallel work of 5 agents:

1. Financial Agent: analysis of 3 years of reporting
2. Legal Agent: checking litigation, restrictions
3. HR Agent: personnel structure, turnover
4. Market Agent: market position, competitors
5. Risk Agent: synthesis of risks from all sources

Execution graph:

• t1 (financial), t2 (legal), t3 (hr), t4 (market) — parallel
• t5 (risk) — depends on t1, t2, t3, t4
• t6 (final_report) — depends on t5

Results:

• DD time: 4 weeks → 3 days (9x faster)
• Aspect coverage: 78% → 94%
• Cost per DD reduced by 71% — savings amount to tens of thousands of dollars per project. For large corporations, savings can reach hundreds of thousands. For example, one client saved $70,000 annually.

Let's compare approaches:

Task distribution among agents

Monitoring and tracing

import mlflow

def log_orchestration_run(state: OrchestratorState):
    with mlflow.start_run():
        mlflow.log_metrics({
            "total_tasks": len(state["task_plan"]),
            "completed_tasks": sum(1 for t in state["task_plan"] if t["status"] == "completed"),
            "failed_tasks": sum(1 for t in state["task_plan"] if t["status"] == "failed"),
        })
        mlflow.log_text(json.dumps(state["task_plan"], indent=2), "task_execution_log.json")

Scope of work

• Designing a multi-agent system architecture for your task
• Implementing the orchestrator using LangGraph or a comparable framework
• Developing specialized AI agents (up to 10) integrated with your data
• Configuring parallel execution, retry logic, and fallback
• Monitoring and tracing via MLflow, Weights & Biases
• Documentation: graph schema, agent APIs, operation manual
• Training your team (2–3 sessions)

Estimated timeline

• Orchestrator design: 1–2 weeks
• Implementation of base agents (3–5): 3–5 weeks
• Parallel execution integration: 1 week
• Error handling and monitoring: 1–2 weeks
• Total: 6–10 weeks

We guarantee stable production operation. All solutions are covered by unit tests and integration tests. We have 10+ years of experience in AI/ML and have implemented orchestrators for financial due diligence, report automation, and content generation.

We offer turnkey AI agent orchestrator development in 6–10 weeks. Write to us for a free project assessment. Contact us — we will evaluate your project, select architecture, and determine implementation timelines.