Enterprise Orchestration for Harnessing Agentic AI: A Strategic Imperative

Agentic AI stands poised to revolutionize enterprise operations. These autonomous software agents are capable of perceiving their environment, making decisions, and taking actions to achieve specific goals. With these capabilities, agentic AI promises to unlock significant efficiencies and competitive advantages.

However, if it’s not managed within a robust and structured framework, the very autonomy that makes agentic AI so powerful can also introduce substantial risks. In a previous blog post, Agentic AI is Here, we examined why enterprise orchestration is critically relevant as organizations adopt agentic AI.

Without a sophisticated orchestration layer, organizations risk operational chaos, compromised governance, and an inability to integrate advanced AI capabilities into the existing workloads and business logic that have been established over the course of decades. Key features such as comprehensive observability, inherent scalability, robust governance mechanisms, detailed reporting, and seamless integration are not merely desirable but are now foundational prerequisites. It is only with these orchestration capabilities that organizations can harness the true potential of agentic AI, while mitigating its inherent complexities and risks.

In this post, we will dive deeper into the enterprise orchestration capabilities required for successful agentic AI initiatives.

The rise of agentic AI and the looming orchestration gap

The promise of agentic AI: A new paradigm in automation

Agentic AI represents a significant leap beyond traditional automation. The global market is currently $5.2B and is expected to expand at a compound annual growth rate of 44% and reach $196.6B by 2034. Unlike scripted bots or rule-based systems, AI agents can:

• Learn and adapt. Adjust their behavior in response to new data and changing circumstances.
• Reason and plan. Formulate complex strategies to achieve multi-step goals.
• Interact autonomously. Engage with digital environments, other systems, and even other agents, with minimal human intervention.
• Manage complex workloads. Dynamically allocate resources, prioritize tasks, and optimize processes in real time.

The "wild west" scenario: The perils of un-orchestrated agents

Fewer than one percent of enterprise software applications harnessed agentic AI in 2024, while projections suggest that by 2028 nearly 33% will.  The deployment of autonomous AI agents without a robust orchestration framework is a recipe for significant operational risk and chaos. Consider the following challenges:

• Lack of visibility and control. Without centralized oversight, how can an operations group track what dozens, hundreds, or even thousands of autonomous agents are doing? How can teams understand agents’ decisions, measure their performance, or audit their actions?
• Resource contention and conflicts. Acting independently, multiple agents could compete for the same limited resources (such as API rate limits, database connections, and processing power), leading to system degradation or deadlock. Conflicting goals between agents could result in suboptimal or even counterproductive outcomes.
• Debugging and error resolution. When an orchestrated process fails, traditional systems offer logs and defined states for diagnosis. If an autonomous agent makes an error or an unexpected decision, pinpointing the cause within its complex internal logic becomes exceedingly difficult, especially without comprehensive observability.
• Compliance and governance breaches. How can an organization ensure that autonomous agents adhere to regulatory requirements, internal policies, and ethical guidelines? If governance isn’t embedded within an orchestration layer, agents could inadvertently access sensitive data, perform unauthorized actions, or create biased outcomes.
• Spiraling chaos from unmanaged autonomy. The cumulative effect of unmanaged agent activity can lead to unpredictable system behavior, cascading failures, and an overall loss of strategic control over critical business processes. The modernization of legacy workloads could ultimately have an adverse impact, making services more opaque and difficult to manage.

Bridging the gap: The enduring relevance of enterprise orchestration

The discipline of enterprise orchestration has been honed over decades, enabling the efficient management of complex IT estates and distributed applications. This discipline provides the foundational principles and proven methodologies essential for taming the complexities of agentic AI. While the “actors” in agentic AI grow more intelligent, the fundamental requirements for coordination, management, and oversight only get more critical.

Core pillars of enterprise orchestration for agentic AI

To successfully integrate and scale agentic AI, organizations must leverage and extend their enterprise orchestration capabilities, focusing on the following core pillars:

1. Comprehensive observability

To gain true insight into agentic AI systems, operations groups need more than just basic logging. An effective orchestration platform must address these requirements:

• Real-time monitoring. Continuous tracking of agent activities, decision-making processes, resource utilization, and interactions with other systems.
• Explainable AI (XAI) integration hooks. Mechanisms to understand why an agent made a particular decision, which is crucial for debugging, cultivating trust, and making refinements.
• Audit trails. Immutable records of all agent actions, communications, and state changes are required for compliance, security forensics, and accountability.
• Performance metrics. Dashboards and alerts on key performance indicators (KPIs) for individual agents and the overall agentic ecosystem, such as task completion rates, error rates, resource efficiency, and SLAs.

2. Inherent scalability and resilience

As agentic AI deployments grow, the orchestration layer must scale seamlessly and ensure continuous operation. This requires these capabilities:

• Dynamic resource allocation. Intelligently assigning and re-assigning computing, network, and data resources to agents based on demand and priority.
• Load balancing and fault tolerance. Distributing workloads across agents and systems, with built-in mechanisms for failover and recovery to eliminate exposure to single points of failure.
• Lifecycle management. Tools for deploying, updating, versioning, and retiring AI agents and their associated models in a controlled and scalable manner.

3. Robust governance and control

Autonomy must be balanced with rigorous governance. Orchestration provides the framework for these capabilities:

• Policy enforcement. Defining and enforcing operational boundaries, access controls, ethical guidelines, and business rules for AI agents.
• Human-in-the-loop (HITL) mechanisms. Integrating checkpoints for human review, approval, or intervention, for example, in the event of critical agent decisions or exceptional situations.
• Compliance management. Delivering comprehensive reporting and controls that help ensure agent activities align with internal compliance mandates and industry regulations, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA).
• Identity and access management for agents. Treating agents as distinct identities with defined permissions, similar to human users or service accounts.

4. Seamless integration with existing business workload logic

Agentic AI cannot operate in a vacuum. It must connect with and enhance the business processes and legacy systems that have been established over the course of decades. Here are a few key requirements:

• API management and service orchestration. Providing robust interfaces for agents to interact with existing enterprise applications, databases, and third-party services.
• Data transformation and flow management. Ensuring data consumed and produced by agents is compatible with existing data formats and workflows.
• Workflow continuity. Orchestrating end-to-end processes that may involve a combination of AI agents, traditional automated tasks, and human activities, ensuring smooth handoffs and consistent business logic execution.
• Integration to avoid silo creation. Preventing agentic AI from becoming another isolated technology stack by integrating its orchestration with broader enterprise IT management.

5. Advanced reporting and analytics

Beyond operational metrics, the following capabilities for gaining strategic insights are crucial:

• Business value reporting with SLA support. Quantifying the impact of agentic AI on business outcomes, ROI, and efficiency gains, while providing derived SLAs based on historical run data.
• Risk and compliance reporting. Generating automated reports and user-friendly dashboards on agent adherence to policies and regulations, which can be delivered to auditors and stakeholders.
• Trend analysis. Identifying patterns in agent behavior, performance, and resource consumption to inform optimization strategies and future development.

The strategic imperative: Orchestration as the bedrock for agentic AI success

The journey towards leveraging agentic AI is not merely about developing intelligent agents; it is fundamentally about integrating this intelligence into the operational fabric of the enterprise, and doing so in a controlled, scalable, and beneficial manner. Indeed, respondents in 94% of organizations cite process orchestration as a crucial component of their larger tech stack for achieving AI success.  Neglecting the orchestration layer is akin to building a fleet of powerful, autonomous vehicles without roads, traffic signals, or navigation systems—the potential for chaos and underutilized capabilities is immense.

Reliable orchestration: A strategic enabler, not an afterthought

Leaders must view enterprise orchestration as a strategic enabler for agentic AI, not as a secondary concern or a problem to be solved later. Planning for robust orchestration must occur in parallel with the development and deployment of AI agents. This proactive approach will yield these gains:

• Eliminate risk of AI initiatives. By providing guardrails, visibility, and control from the outset. Currently, while 77% are working on AI governance, only 14% effectively enforce AI governance policies.
• Accelerate adoption. By offering a structured environment for deploying and managing agents.
• Maximize ROI. By ensuring agents operate efficiently and in alignment with business goals, and enhance existing processes rather than disrupting them.

Conclusion

Agentic AI holds the key to unprecedented levels of business agility and innovation. However, this power demands that implementation groups and leaders take a structured, responsible approach. Established, reliable, and scalable enterprise orchestration is not just beneficial but an absolute imperative for realizing the promise of agentic AI. Without it, organizations are likely to squander their AI investments, be exposed to unacceptable operational risk, and fail to integrate these powerful new capabilities into core business services. By leveraging robust, scalable, and proven orchestration, enterprise leaders can confidently embark on their agentic AI journey, setting the foundation for a more intelligent, automated, and resilient future.

Stay tuned for the next blog in this three-part series, which will discuss an agentic use case that leverages proven enterprise orchestration frameworks. You can read the first post here.

For more on the Automation by Broadcom team's perspective on AI and automation, including an in depth demonstration, watch the webinar: AI-Powered Automation: Automic Automation and Beyond.