AI Agent Orchestration: From Prototype to Production

Your agent works in a notebook. It reasons, calls tools, produces good output. Then you deploy it. A network timeout kills the run midway through a five-step workflow. No state was saved. No retry fires. The user sees nothing---no error, no partial result, no explanation. The agent just disappears.

This is not a rare edge case. It is the default outcome for most agent deployments. Gartner predicts that over 40% of agentic AI projects will be canceled by 2027 due to unanticipated complexity and cost. McKinsey reports fewer than 10% of organizations have successfully scaled agents in any individual function.

The gap between prototype and production is not intelligence. It is infrastructure.

The Four Missing Pieces

When an agent runs in a notebook or a local script, four things are silently handled by the developer sitting at the keyboard: state management, failure recovery, execution visibility, and autonomy control. Remove the developer, and these responsibilities have no owner.

State Loss

A production agent workflow might span minutes or hours. During that time, the process can crash, the server can restart, or the LLM provider can return a timeout. Without durable state, every interruption means starting over---or worse, producing inconsistent results from a half-completed execution.

No Retry Semantics

LLM API calls fail. Tool calls fail. External services fail. In a prototype, the developer retries manually. In production, you need exponential backoff, configurable retry policies, dead-letter queues for permanently failed tasks, and idempotency guarantees so retries don’t produce duplicate side effects.

Invisible Execution

When an agent runs autonomously, someone needs to answer: What step is it on? What did it decide at each branch? How long has it been stuck? Why did it fail? Without execution tracing and observability, debugging a multi-step agent is forensic archaeology.

Uncontrolled Autonomy

Prototype agents run with full autonomy because the developer is watching. Production agents need an autonomy spectrum---some decisions the agent makes alone, some require human approval, some are forbidden entirely. Without this spectrum, you get either an agent that can’t do anything useful or one that does things it shouldn’t.

The Agent-to-Worker Pattern

The foundational pattern for production agents is simple: wrap each agent as a task inside a workflow engine.

Instead of writing a monolithic agent that handles everything from start to finish, you decompose the work into discrete steps. Each step becomes a “worker” that the orchestration platform manages. The workflow engine handles the transitions, retries, state persistence, and visibility.

This is the same pattern that companies like Netflix, Uber, and Stripe have used for years to manage complex distributed processes. The difference is that now some of those workers are LLM-powered agents instead of deterministic code.

What this looks like in practice:

Define the workflow as a directed graph of steps
Wrap each agent as a worker that receives input, executes, and returns output
Let the engine handle state persistence, retries, timeouts, and routing
Add human-in-the-loop steps as explicit approval gates in the graph

The workflow engine doesn’t need to understand what the agent is doing. It only needs to manage when it runs, what happens if it fails, and where to route the output.

Durable Execution: RAM for Workflows

The concept that unifies modern orchestration is durable execution---the guarantee that a workflow’s state persists across failures, restarts, and deployments.

Think of it as RAM for long-running processes. When your computer crashes, you lose everything in memory. Durable execution is the equivalent of your RAM being backed by persistent storage---every variable, every decision point, every intermediate result survives the crash.

For AI agents, this means:

Mid-workflow failures resume from the last completed step, not from the beginning
LLM responses are cached so retries don’t repeat expensive API calls
Tool call results persist so external side effects aren’t duplicated
Human approval states survive across sessions---an approval requested on Monday can be granted on Tuesday without losing context

This is not a nice-to-have. Without durable execution, any agent workflow longer than a single API call is fragile by design.

The Human-in-the-Loop Spectrum

Production agents need configurable autonomy. Anthropic’s framework describes three positions on the spectrum:

Human-in-the-loop: The agent proposes actions. A human approves each one before execution. Highest safety, lowest throughput. Appropriate for high-stakes decisions---financial transactions, medical recommendations, legal determinations.

Human-on-the-loop: The agent executes autonomously but a human monitors in real time and can intervene. The agent proceeds unless stopped. Appropriate for medium-risk workflows where speed matters but errors have real consequences.

Human-out-of-the-loop: The agent operates fully autonomously. Humans review outputs after the fact. Appropriate only for well-tested workflows with low-stakes outcomes and robust monitoring.

The critical insight: this is not a one-time architectural decision. Different steps within the same workflow can have different autonomy levels. An agent might autonomously gather data (out-of-loop), generate a recommendation (on-the-loop), and then wait for human approval before executing a transaction (in-the-loop).

Orchestration engines make this practical by modeling human approvals as explicit workflow steps with timeouts, escalation paths, and audit trails.

Tool Landscape: Three Approaches

The market for agent orchestration has converged around three distinct architectures. Each makes different trade-offs.

Temporal: Code-First Durable Execution

Temporal provides durable execution as a programming primitive. You write workflows as regular code in Go, Python, Java, or TypeScript. Temporal’s runtime transparently persists state, replays failed executions, and guarantees exactly-once semantics.

Strengths: Deep language integration. Workflows are real code---testable, debugable, versionable. OpenAI chose Temporal as the execution backbone for Codex, their coding agent. Grid Dynamics migrated their production AI research system from LangGraph to Temporal after hitting reliability limits.

Trade-offs: Requires infrastructure management (or Temporal Cloud). Steeper learning curve. No native AI primitives---you build those yourself or integrate with agent frameworks.

Best for: Engineering teams that want full control over orchestration logic and already manage distributed systems.

Orkes Conductor: Managed Orchestration with AI Primitives

Conductor originated at Netflix for microservice orchestration and is now maintained by Orkes as a managed platform. It provides a visual workflow builder, native LLM integration tasks, and built-in support for human-in-the-loop patterns.

Strengths: Lower operational burden. Native AI task types (LLM calls, RAG, vector search) reduce integration code. Visual workflow editor makes non-trivial flows easier to reason about. Used by organizations like Tesla and American Express.

Trade-offs: Less flexible than code-first approaches. Vendor dependency on the managed platform. Visual editors can become unwieldy for highly dynamic agent behaviors.

Best for: Teams that want managed infrastructure and prefer declarative workflow definitions over imperative code.

LangGraph: Agent-Native State Machines

LangGraph, from the LangChain ecosystem, models agents as state machines with explicit state, branching, and cycling. It is purpose-built for agent workflows rather than adapted from general orchestration.

Strengths: Native understanding of agent patterns---tool calling, reflection loops, multi-agent coordination. Tight integration with LangChain’s tool and model abstractions. Lower barrier to entry for teams already in the LangChain ecosystem. LangGraph Platform provides managed deployment.

Trade-offs: Less mature for long-running workflows that span hours or days. Reliability guarantees are weaker than Temporal’s at scale. Grid Dynamics found that LangGraph’s state management created challenges in complex production scenarios.

Best for: Teams building agent-first applications who want agent-native abstractions and are comfortable with the LangChain ecosystem.

The Two-Layer Architecture

A pattern emerging in production systems combines orchestration and agent layers:

Orchestration layer (Temporal or Conductor): Manages the overall workflow, state persistence, retries, human-in-the-loop gates, and cross-service coordination
Agent layer (LangGraph or custom): Manages the reasoning logic within each step---tool selection, reflection, multi-turn interactions

This separation keeps each layer focused. The orchestration layer doesn’t need to understand LLM reasoning. The agent layer doesn’t need to handle distributed systems concerns. Grid Dynamics adopted exactly this pattern, using Temporal to orchestrate workflows that contain LangGraph-powered agent steps.

Decision Framework

Choosing the right tool depends on what you’re building:

Scenario	Recommended Approach
Single agent, simple flow	LangGraph standalone
Multi-agent, short-lived tasks	LangGraph with LangGraph Platform
Long-running workflows with human approval	Temporal or Conductor
Existing microservice orchestration	Add agent workers to existing Conductor/Temporal
Mission-critical with compliance requirements	Temporal (strongest durability guarantees)
Team wants managed infrastructure, minimal ops	Orkes Conductor Cloud or LangGraph Platform
Complex system combining all patterns	Two-layer: Temporal/Conductor + LangGraph

The wrong choice is not picking the wrong tool. The wrong choice is deploying agents without any orchestration layer.

Production Challenges Beyond Orchestration

Orchestration solves the structural problem. Four other production concerns remain.

Observability

You cannot operate what you cannot see. Production agents need:

Distributed tracing (OpenTelemetry) across every step, tool call, and LLM interaction
Token and cost tracking per workflow, per step, per model
Latency monitoring with alerting on degradation
Decision logging that captures what the agent considered, not just what it chose

Without observability, you are running a black box in production. When it fails---and it will---you will have no basis for diagnosis.

Resilience

Beyond retries, production agents need:

Circuit breakers that stop calling a failing LLM provider
Fallback strategies (model B when model A is down, cached responses when all models fail)
Graceful degradation that returns partial results instead of nothing
Timeout hierarchies (step-level, workflow-level, and session-level)

State Management

Agent state is more complex than traditional workflow state:

Conversation history that grows with each interaction
Tool call results that may be large (documents, datasets)
Intermediate reasoning that informs later steps
Context windows that have hard limits

State management intersects with context engineering. The orchestration layer must manage what enters and exits the agent’s context at each step, compressing history and paging large results to external storage.

Security and Access Control

Production agents interact with real systems:

Credential management for tools and APIs (never hardcoded, always rotated)
Least-privilege execution (each agent step gets only the permissions it needs)
Audit trails that satisfy compliance requirements
Input validation to prevent prompt injection through tool inputs
Output sanitization before results reach users or downstream systems

Real-World Evidence

The theory becomes concrete in production deployments.

Healthcare --- Florence Healthcare + Orkes Conductor: Uses Orkes Conductor to orchestrate agents for clinical trial document management. The workflow spans multiple approval gates, regulatory checks, and document generation steps. Without durable execution, a single timeout in the regulatory check step would require restarting the entire multi-hour workflow.

Cybersecurity --- SOC Automation (IBM ATOM, Microsoft Copilot for Security): Security operations centers use orchestrated agent workflows for threat triage. An alert triggers a workflow that enriches the alert with threat intelligence, correlates with historical incidents, generates a severity assessment, and routes to the appropriate response team. Microsoft Copilot for Security achieved a 0.87 F1 score in incident triage. Each step must be auditable. Human-in-the-loop gates control escalation decisions.

Deep Research --- Grid Dynamics: Built a production AI research system initially on LangGraph. As complexity grew---multi-step research spanning documents, APIs, and knowledge bases---they migrated the orchestration layer to Temporal while keeping LangGraph for individual agent reasoning steps. The result: reliable execution of research workflows that previously failed intermittently under load.

These cases share a pattern: the agent intelligence was never the bottleneck. The infrastructure around the agent determined success or failure.

Orchestration Is Governance

Here is what most discussions about agent orchestration miss: orchestration is not just an engineering concern. It is governance infrastructure.

Every governance requirement maps to an orchestration capability:

Auditability requires execution logging and decision tracing
Compliance requires human-in-the-loop gates and access controls
Reliability requires durable execution and retry policies
Accountability requires clear ownership of each workflow step
Controllability requires the autonomy spectrum---knowing which decisions an agent can make and which it cannot

Organizations that treat orchestration as plumbing and governance as policy are building two separate systems that should be one. The orchestration layer IS the governance layer. It is where policies become executable, where oversight becomes operational, and where accountability becomes traceable.

This is why we advise clients to start with orchestration architecture before they start with agent capabilities. Get the infrastructure right, and governance comes built-in. Get the agent working first, and governance becomes a retrofit---expensive, fragile, and incomplete.

The 40% cancellation rate Deloitte reports is not a technology failure. It is a governance failure. And the fix is not better models or smarter prompts. It is better orchestration.

Victorino Group helps organizations build governed AI agent systems---from architecture through production. If your agents work in demos but not in production, the problem is likely not the agent.