AI agents are providing organisations with new opportunities to improve operational efficiency, accelerate decisions and deliver better customer experiences. But as businesses introduce agents across different functions, an important question emerges: how can leaders ensure these capabilities work together to deliver lasting value?
As agents begin to share information, coordinate actions or contribute to the same business process, the challenge becomes increasingly complex.
To solve this, leaders cannot simply deploy more intelligent agents. Instead, they must consider how those agents connect, how their activities are governed and whether the wider architecture can support business requirements efficiently.
Why disconnected AI agents can limit enterprise value
Individual AI agents can deliver meaningful results, particularly when applied to clearly defined business problems. However, success in one workflow does not automatically translate into value across the enterprise.
For example, a customer enquiry may require information from several departments. One agent might retrieve account details, another might investigate a transaction and a third might prepare a recommended response. Without appropriate agent coordination, connecting these activities could require additional integrations, permissions and monitoring arrangements.
As organisations expand their use of agentic AI, this can create three challenges.
- Greater operational complexity. Independently developed agents may duplicate connections to enterprise systems or rely on different approaches to managing information. Maintaining these capabilities as business processes change can increase engineering effort.
- Fragmented governance. When agents operate across systems, organisations need to understand what each can access, which actions are permitted and where human oversight is required. Inconsistent controls can make accountability more difficult.
- Limited visibility into performance and cost. Different models, tools and platforms may make it harder to evaluate how complete workflows perform and identify where resources are being consumed.
These concerns extend beyond theoretical architecture discussions. According to Gartner, only 13% of organisations believe they have the right AI agent governance in place. Gartner also predicts that an average global Fortune 500 enterprise will have more than 150,000 agents in use by 2028. Without a coordinated approach to managing agent identities, permissions and activity, organisations risk creating an increasingly complex environment that becomes harder to oversee and control.
How an agentic fabric supports connected enterprise AI
One way to address this challenge is through an agentic fabric, a shared architectural layer that helps connect AI agents, models, enterprise systems and data.
Rather than treating every agentic application as a separate environment, an agentic fabric can provide common capabilities for orchestration, model selection and governance.
Coordinating work across agents and systems
Completing a task may require information retrieval, analysis, system interactions and human approval. Shared orchestration capabilities can help coordinate these activities, directing work between specialised agents and existing enterprise applications.
However, coordination must be designed carefully, always aiming to introduce the level of coordination a workflow actually needs, rather than increasing complexity for its own sake.
Selecting models according to business needs
Different tasks place different demands on AI models. Extracting structured information from a document may require different capabilities from analysing an operational exception or preparing a recommendation for specialist review.
An agentic fabric supports model routing based on task requirements, including accuracy, latency, cost and data-handling constraints. This flexibility can help organisations evaluate where specialised or smaller models may be appropriate and where more advanced reasoning capabilities are necessary.
Establishing consistent governance
Organisations need clear controls over agent activities, such as defining access permissions, monitoring actions, recording relevant decisions and establishing when human intervention is required.
Shared governance capabilities can support greater consistency across agentic workflows, but they must operate alongside existing enterprise security, risk and accountability arrangements.
Building the architecture behind scalable agentic AI
An effective agentic fabric depends on the strength of the wider technology environment. Organisations need access to suitable models, reliable agent execution environments, enterprise systems and compute infrastructure that can support their workloads.
Cloud and AI services providers, such as AWS, can support the development and operation of agentic applications. For example, Amazon Bedrock offers access to foundation models, while its AgentCore offering provides capabilities including agent runtime, identity, policy controls, tool integration and observability. Amazon SageMaker AI provides additional capabilities for organisations developing and deploying specialised machine-learning models.
Leaders must also consider the underlying compute environment. Agentic workflows may involve multiple model interactions, data retrieval operations, tool calls and processing steps. The performance and cost of the overall workflow therefore depend on more than model inference alone.
AMD provides compute technologies that can support different elements of AI workloads. AMD EPYC server processors can support general-purpose processing, data handling and suitable inference workloads, while AMD Instinct accelerators offer options for workloads requiring dedicated AI acceleration.
The appropriate combination depends on factors such as model requirements, latency, throughput, software compatibility and deployment constraints.
From connected architecture to business value
The opportunity for agentic AI across the enterprise now lies in ensuring coordinated, effective systems. An agentic fabric can help organisations achieve this, bringing together orchestration, model selection and governance, supported by cloud services and infrastructure suited to operational requirements.
However, the architecture itself is not the outcome. Organisations still need to establish whether their chosen approach can deliver measurable value while meeting the performance, security and governance requirements of real business processes.
In our whitepaper, Production-Ready Agentic AI Prototypes for Regulated Industries , we explored how organisations can evaluate these requirements before committing to wider deployment. Read now to learn how production-ready prototyping can support more informed agentic AI investment decisions.
