Top 10+ Agentic Orchestration Frameworks & Tools

79% of executives are already adopting AI agents, although 19% of firms struggle with coordination.¹ They cannot manage agents across different applications. Agentic orchestration offers the solution.

Explore agentic orchestration is, its patterns, and the top frameworks that enable multi-agent collaboration.

What is agentic orchestration?

Agentic orchestration coordinates autonomous AI agents within a unified system to complete complex tasks and structured tasks across multiple systems and domains. It builds on earlier forms of automation by enabling multi-agent orchestration, where multiple agents collaborate under an orchestration layer that governs communication, task planning, and execution.

Unlike static automation scripts, agentic orchestration leverages generative AI and AI models to adapt to context, minimize the need for human intervention, and enable seamless execution across diverse systems.

Core principles 

1. Autonomy: Agents can act independently within their defined roles, supported by function calling to external systems.
2. Collaboration: Multiple AI agents communicate to resolve complex problems, distribute multiple tasks, and achieve end-to-end automation.
3. Alignment: Systems maintain consistent objectives and ensure compliance with organizational and regulatory requirements in highly regulated industries.
4. Observability: Logs, monitoring tools, and evaluations enable continuous monitoring and continuous optimization.
5. Human oversight: Human-in-the-loop approaches combine automation with human input in high-risk or ambiguous contexts.

Orchestration patterns

Agentic orchestration can be categorized into several patterns based on how agents are coordinated within a system. These patterns determine the flow of tasks, the communication between agents, and the overall system architecture.

Centralized orchestration

In this pattern, a single manager or router agent is responsible for assigning tasks, controlling the workflow, and ensuring that objectives are met. The manager acts as a central hub, directing tasks to specialized agents based on predefined rules or a dynamic plan.

Specific patterns within this category include:

• Sequential orchestration: A linear pipeline where a manager directs tasks through a fixed, step-by-step sequence of agents. This is ideal for processes with clear dependencies, like data processing pipelines.

• Magentic orchestration: A more advanced form of centralized control where the manager agent dynamically builds and refines a plan to solve complex, open-ended problems. The manager directs and delegates tasks as needed to a team of specialized agents.
• Hierarchical orchestration: A scalable, tiered structure where a manager-subordinate relationship is used to handle complex tasks across multiple departments or teams.

Decentralized orchestration

This pattern eliminates the single point of control, enabling multiple agents to interact directly and complete a complex task. This approach enhances resilience and offers greater flexibility for collaborative problem-solving.

Specific patterns within this category include:

• Group chat orchestration: Agents collaborate through a shared conversation thread, building on each other’s contributions to reach a decision or solve a problem. A chat manager may facilitate the discussion, but agents communicate directly to achieve a consensus.

• Handoff orchestration: Agents dynamically delegate tasks to one another without the need for a central manager. Each agent can assess the task and decide to either handle it or transfer it to another agent with more appropriate expertise, similar to a referral system.

Federated Orchestration

This pattern is helpful for highly regulated or distributed environments. It enables collaboration across different organizational silos or systems while maintaining data governance and security. It often combines elements of both centralized and decentralized approaches to manage a wider network of agents and systems.

Tools and frameworks

Several AI agent frameworks provide the infrastructure for agentic workflows and multi-agent orchestration. Some of them include:

Note that these tools are listed based on the number of GitHub stars they received.

• LangGraph by LangChain: Provides modular design and graph-based workflows for complex workflows and structured tasks.
• MetaGPT by FoundationAgents: Encodes role-based collaboration (e.g., software engineer, QA) to coordinate multiple agents in software development.
• AutoGen by Microsoft: Focuses on conversational collaboration between digital agents, often configured as planner–executor–critic loops.
• CrewAI: Organizes specialized agents into “crews” with role-specific goals, useful for business processes and routine operations.
• Agents SDK by OpenAI: Enables lightweight orchestration and agent handoffs with function calling to external tools.
• CAMEL-AI: Provides modular societies of autonomous AI agents with coordinators for large-scale simulations and complex processes.
• Agent Development Kit by Google: Supports multi-agent orchestration with integrated evaluation, debugging, and deployment capabilities.
• Langroid: Implements an actor-model style for multi-agent orchestration, emphasizing modularity and delegation.
• BeeAI: Emphasizes interoperability through the model context protocol and integration of third-party agents for seamless integration.
• Azure AI Foundation Agent Service: Enables the operation of agents across development, deployment, and production by abstracting infrastructure complexity.

Compare these frameworks and learn their core capabilities:

• Agentic frameworks benchmark
• Multi-agent systems and tools

Agentic orchestration applications

Agentic orchestration is the critical capability that transforms individual agents into a cohesive, goal-oriented system. The following are real-world applications where multi-agent systems coordinate to deliver business value.

Business processes

Agentic orchestration enables end-to-end automation across multiple departments and systems. It coordinates specialized agents to handle complex, multi-step workflows without manual handoffs.

• Human resources: Orchestrates a team of agents to manage the entire employee lifecycle, from onboarding and policy Q&A to workforce management and offboarding.
• Customer onboarding: 
• Customer operations: Orchestrated systems improve service quality by managing customer interactions across channels, with a group of agents handling initial queries, providing information from different databases, and handing off complex issues to a human-in-the-loop for verification.

Explore AI agents for workflow automation

Supply chain

Agentic orchestration enhances supply chain management by coordinating multiple, specialized agents to manage and optimize a complex network of planning, sourcing, logistics, and inventory management.

• Predictive maintenance: An orchestration platform coordinates agents to analyze real-time equipment data, predict potential failures, and automatically trigger a maintenance agent to schedule a repair or order new parts.
• Inventory management: Agents are orchestrated to track stock levels, automatically reorder supplies when a threshold is met, and communicate with logistics agents to handle real-time disruptions like shipping delays.
• Supplier onboarding: A coordinated system of digital agents handles the entire process, from running compliance checks and generating contracts to integrating new suppliers into the company’s existing workflows.

Enterprise systems

Agentic orchestration provides the core logic for AI-driven processes that require seamless collaboration across different enterprise platforms, such as ERP, CRM, and RPA.

• Purchase-to-pay: A series of orchestrated agents manages the full procurement cycle, from a purchasing agent placing an order to an accounts payable agent processing the invoice for payment, cutting cycle times and boosting transparency.
• Order-to-cash: A multi-agent system speeds up the entire journey from order receipt to payment by coordinating agents that handle order processing, fulfillment, and accounts receivable, improving cash flow and customer satisfaction.
• Dispute resolution: An orchestrated workflow automates claim and chargeback tracking by having one agent gather information, another analyze the dispute, and a third communicate the resolution, simplifying the process and making it faster.

Explore how AI agents are used in enterprise systems, such as:

• Agentic AI ERP
• SAP AI Agents
• Oracle AI Agents

Banking and Financial Services

In this sector, orchestration is utilized for complex, risk-sensitive workflows that necessitate multiple agents collaborating to ensure accuracy and compliance.

• Regulatory compliance: A coordinated system of agents enforces compliance by validating customer information against watchlists, flagging discrepancies, and maintaining a transparent audit trail of every action for regulatory review.
• Loan and mortgage processing: An orchestrated workflow enables a group of agents to handle the entire loan approval process—from gathering and verifying documents to applying financial models and providing final authorization for review by a human analyst.
• Fraud detection and prevention: This is a classic example of orchestration, where one agent monitors transactions, another identifies and flags suspicious activity, and a third freezes the account and generates an incident report for a human security team.

Check out how AI agents and agentic LLMs are utilized in finance:

• AI Agents in Accounting
• Benchmark 30 Finance LLMs
• AI-Based Stock Trading

Energy and utilities

Agentic orchestration allows for the management of highly distributed and complex systems, such as power grids and workforce management, by enabling specialized agents to communicate and act in real-time.

• Grid management: A multi-agent system with distinct agents for generation stations, distribution hubs, individual smart meters, and smart grid solutions works together to balance energy supply and demand, optimize distribution, and prevent outages.
• Meter-to-cash: An orchestrated meter-to-cash process can automate the entire billing cycle, coordinating agents that handle automated meter reading, bill generation, and payment collection to improve accuracy and efficiency.
• Workforce management: An orchestration system optimizes how field technicians are scheduled and deployed by having agents coordinate to track technician availability, assign tasks based on location and skill, and provide real-time updates on job progress.

Telecom

In telecom, orchestration is used to manage and automate large-scale, complex networks and customer-facing operations.

• Network operations: A coordinated system of agents monitors different parts of the network to automatically detect faults, diagnose the problem, and trigger a series of actions to resolve it, ensuring network reliability and minimizing downtime.
• Customer onboarding: Orchestration speeds up the process by having agents coordinate to handle SIM activation, device setup, and service enablement, providing a seamless customer experience from start to finish.
• Billing and revenue management: An orchestrated workflow automates complex billing adjustments, payments, and refunds by having specialized agents manage each step, which boosts accuracy and customer satisfaction.

• Multi-Agent Systems: Applications & Comparison of Tools

Benefits

• Operational efficiency: Streamlines routine operations, reduces costs, and enhances scalability.
• Operational agility: Enables dynamically responding to real-time data and disruptions.
• Seamless collaboration: Ensures cooperation between agents, humans, and multiple systems.
• Competitive advantages: Supports innovation while allowing AI systems to operate alongside human staff.
• Improved satisfaction: Drives superior customer experiences and measurable improvements in service quality.

Challenges

• Governance: Requires robust data governance to prevent risks from multiple agents interacting with diverse systems.
• Compliance: Systems must ensure compliance in highly regulated industries, especially in finance and healthcare.
• Human oversight: Effective deployment requires clear thresholds for human intervention and escalation.
• Seamless integration with existing workflows and legacy systems remains a significant barrier. These older systems may be built on outdated architectures that are not compatible with modern AI technologies.

Further reading

Discover more on Agentic AI by checking out:

• The 7 Layers of Agentic AI Stack
• AI Agents vs Agentic AI Systems
• 4 Agentic AI Design Patterns & Real-World Examples

External Links

• 1. AI agent survey: PwC .
• 2. ResearchGate - Temporarily Unavailable.
• 3. https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/ai-agent-design-patterns
• 4. Source 3
• 5. Source 3
• 6. Source 3
• 7. Federated Search | Search Orchestration | BA Insight.