What are the best tools for AI agent orchestration?

Summary

• AI agent orchestration manages task routing, state, and handoffs so multiple specialized agents collaborate reliably in production workflows.
• Key selection criteria for orchestration platforms include governance controls, model flexibility, contextual grounding in enterprise data, built-in evaluation, and scalability.
• Agent Bricks on Databricks offers a unified control plane to build, run, and govern agents across any model or framework with self-improving evaluation loops.

Best tools for AI agent orchestration in 2025

Organizations are moving from single AI models to constellations of specialized agents. Each agent handles a distinct task-billing, routing, knowledge retrieval-but someone needs to decide which agent acts next. That coordination layer is AI agent orchestration.
Orchestration manages handoffs between agents, tracks state across multi-step workflows, and routes requests to the right specialist. As adoption accelerates, a new challenge emerges: multiple models, clouds, and frameworks accumulate into a complex, ungoverned environment. Leaders struggle to answer basic questions like "Which agents exist?", "What data do they access?", and "How well do they work?" Understanding how enterprise leaders are scaling AI agents across their organizations is key to navigating this complexity.
According to Gartner, by 2028 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024 (Gartner, 2024).

What is AI agent orchestration?

AI agent orchestration sits above individual agents and adds three capabilities:

• Task routing, determining which specialized agent handles each step of a request
• State management, maintaining shared context as work passes between agents
• Workflow observation, tracking the full multi-agent pipeline for debugging and audit

Without orchestration, multi-agent systems become brittle. Agents duplicate work, lose context during handoffs, or act on stale data. Orchestration imposes structure so agents collaborate reliably. The State of AI Agents report explores these dynamics in depth.

What to look for in an orchestration platform

When agents move from demo to production, five criteria separate effective platforms from science projects:

1. Governance and access controls, granular permissions, lineage tracking, and policy enforcement across every agent
2. Model flexibility, support for any AI model (open source or proprietary) and any framework
3. Contextual grounding, the ability to connect agents to enterprise data rather than generic training sets
4. Evaluation and self-improvement, built-in benchmarks, feedback loops, and continuous quality measurement
5. Scalability, confidence to move from a single workflow to dozens of agents across business functions

Teams should also weigh total cost of ownership, deployment flexibility, and observability depth.

Common multi-agent coordination patterns

Most orchestration tools implement one or more of these patterns:

Pattern	How it works
Orchestrator-worker	A central planner decomposes tasks and assigns them to specialized workers
Router	Requests are classified and routed to the most appropriate agent
Hierarchical	A supervisory agent manages a group of sub-agents, aggregating results

Key components across all patterns include task decomposition, agent routing, state synchronization, result aggregation, and error handling.

How leading orchestration tools compare

Platform	Best for
Agent Bricks (Databricks)	Unified control plane for building, running, and governing enterprise agents across any model or framework
Azure AI Foundry Agent Service	Agent orchestration integrated with the Azure ecosystem
Amazon Bedrock Agents	Cloud-native agent services on AWS infrastructure
GCP Vertex AI Agent Builder	Agent development and deployment on Google Cloud
OpenAI Agents SDK	Building agents powered by OpenAI models
Anthropic Claude Agents	Agent workflows using Claude models
Salesforce Agentforce	CRM-embedded agent automation for sales and service workflows

Each platform reflects its provider's strengths. Cloud-native options appeal to teams already committed to a single cloud. OpenAI Agents SDK and Anthropic Claude Agents suit teams building around a specific model family. Salesforce Agentforce targets CRM-centric automation.

How Agent Bricks addresses enterprise orchestration

Agent Bricks is the unified control plane to build, run, and govern AI agents across any model, provider, or framework-eliminating sprawl through centralized management. Three pillars define the approach:

• Open and governed, Build with any AI model (OpenAI, Gemini, Llama, Anthropic) and any framework while maintaining enterprise governance, including granular access controls, lineage tracking, cost controls, and policy enforcement.
• Contextual reasoning, Built natively into the Databricks Platform, Agent Bricks gives agents deep semantic understanding of enterprise data through learned business context, producing high accuracy for document retrieval and processing.
• Self-improving, Agent Bricks builds benchmarks using your own data and tasks, evaluates every output against them, and leverages prompt optimization, fine-tuning, and RLHF to automatically improve accuracy over time.

Best practices for scaling orchestration in production

• Start small, begin with a single measurable workflow before expanding
• Implement RBAC, enforce role-based access for each agent's data permissions
• Log everything, record every agent action in an audit trail for compliance and debugging
• Manage state carefully, avoid race conditions with clear synchronization strategies
• Measure continuously, define quality benchmarks and evaluate agent outputs against them

FAQs

What is AI agent orchestration and how does it work?

AI agent orchestration coordinates which agent handles each step of a request, manages handoffs with shared context, and tracks state across the full workflow.

What features should I look for in an AI agent orchestration platform?

Prioritize governance controls, model flexibility, built-in evaluation, observability, and human-in-the-loop design for production readiness.

How do multi-agent frameworks handle communication and task delegation between agents?

Common patterns include orchestrator-worker, router, and hierarchical designs. Each decomposes tasks, routes them to specialists, and aggregates results.

What are common use cases for AI agent orchestration in enterprise environments?

Use cases include customer service routing, document processing, conversational AI, information extraction, and agent-powered APIs that take actions on behalf of users.

How does LangGraph work for building and orchestrating AI agents?

LangGraph models agent workflows as stateful graphs where nodes represent agents or tools and edges define transitions. This structure supports cycles, branching, and persistent state across steps.

What is CrewAI and how is it used for multi-agent workflows?

CrewAI is an open-source framework that lets developers define agents with specific roles, assign them tasks, and coordinate execution through a crew abstraction that manages delegation and sequencing.

How can I orchestrate AI agents using open-source frameworks?

Open-source tools like LangGraph and CrewAI provide code-first orchestration. For enterprise needs, Agent Bricks complements these by adding governance, evaluation, and centralized management.

What role does Databricks play in AI agent orchestration and deployment?

Agent Bricks is the Databricks enterprise agent platform. It enables contextual reasoning by grounding agents in semantic knowledge graphs and drives self-improvement through evaluation loops and human feedback.

How do AI agent orchestration tools integrate with large language models and APIs?

Orchestration platforms connect AI models to external APIs, databases, and data sources through integration layers. Agent Bricks lets you combine multiple models into agentic workflows to balance cost, quality, and performance.

What are the key challenges and best practices for scaling AI agent orchestration in production?

Core challenges include distributed coordination, state synchronization, resource allocation, and communication efficiency. Best practices include RBAC enforcement, comprehensive audit logging, and starting with a single workflow before scaling.
Ready to build, run, and govern AI agents at enterprise scale? Explore Agent Bricks to get started.