“It just doesn't work well…”, “There are always failures…” As AI agents begin to take on increasingly complex tasks within organizations, many companies are encountering obstacles. What if the problem isn't the technology or the models' capabilities?
For decades, we have designed companies to coordinate people: departments, managers, procedures, and information systems that reflect traditional hierarchical structures. However, AI agents introduce a new player into the organization. A player capable of executing tasks, making decisions within defined limits, and collaborating with other systems to achieve specific objectives.
Enterprise Memory as Context for AI Agents
When a person joins an organization, they don't just start working with a computer and access to applications. They need to understand procedures, policies, decision criteria, common exceptions, and business objectives. The same applies to AI agents; they need concise access to company information.
Contracts, procedures, records, transactional logs, internal policies, or industry regulations all form part of the enterprise memory that AI relies on.
The problem is that, in most organizations, this memory is fragmented across different systems: ERP, CRM, document management systems, departmental applications, or knowledge repositories.
Navigating this fragmentation often leads these systems to fail. Therefore, before discussing automation, it's necessary to ask whether the organization has an accessible, governed memory prepared to be consumed by intelligent agents. In other words, how can the system itself empower AI.
Importance of Access to Corporate Information in the Field of agentic AI
Many Artificial Intelligence initiatives begin by identifying a specific task that can be automated, such as: classifying documents, answering queries, processing invoices, extracting information from contracts, etc.
While these cases add value, there's a significant risk when it's assumed that transformation will come from adding individual agents, when in reality, businesses operate through processes.
Practical Example in Invoice Management
An agent can automatically extract document data. Another can validate specific fields. However, the complete process requires consulting contracts, verifying purchase orders, applying financial policies, managing exceptions, and coordinating approvals.
This is why an orchestration model is needed.
The Agentic Map: Five Levels for Coordinating People and Agents
One of the most interesting frameworks emerging around autonomous AI is the concept of multi-level orchestration. This means that as an organization's complexity increases, so too must its ability to coordinate agents, processes, and decisions.
Level 1: Specialized Agents
They are responsible for executing specific tasks.
For example:
• Classifying documents.
• Extracting metadata.
• Validating information.
• Generating responses.
Their scope of action is limited and highly specialized.
Level 2: Process Orchestrators
They coordinate multiple agents within a single workflow.
In a document management process, they could oversee everything from document reception to classification, validation, and archiving, ensuring each step is executed in the correct order.
Level 3: Domain Orchestrators
They manage related processes within the same business function.
For example, a financial department could coordinate agents dedicated to billing, payments, auditing, and regulatory compliance under a single framework.
Level 4: Value Chain Orchestrators
Their goal is to optimize complete processes that span different departments.
Here, the vision is no longer functional and individual, but becomes cross-functional.
Level 5: Enterprise Orchestrators
They represent the most strategic level. Their role is to align decisions, priorities, and resources with the organization's overall objectives.
Furthermore, a company with structured AI does not rely on isolated agents, but on networks of coordinated agents through different levels of orchestration. And, naturally, all of them will depend on a common enterprise memory.
Digital Job Descriptions: designing job roles for agents
If organizations define roles and responsibilities for people, why not do the same for agents? One of the most interesting concepts of the agentic model is the Digital Job Description.
Before deploying an agent, it's advisable to answer questions similar to those we would ask when creating a new job role: responsibilities, decision-making authority, information it accesses, tools, workflows between collaborators, etc.
For example, a document agent could have the authority to classify documentation and automatically assign metadata, but not to delete records or modify retention policies.
Defining these limits from the outset not only improves efficiency, but also facilitates governance, auditing, and trust in the system.
Take the test: Is your organization ready to work with agents?
Before integrating new agents, it's worth evaluating some fundamental aspects.
1. Are processes sufficiently documented?
2. Are business rules explicit, or do they depend on the tacit knowledge of certain employees?
3. Is there a single source of truth for critical information?
4. Are document, transactional, and operational systems connected?
5. Are responsibilities and oversight mechanisms clearly defined?
If the answer to several of these questions is no, it would probably be necessary to start by strengthening the information and governance architecture on which the AI will need to operate.
Enterprise memory becomes the foundation that allows agents to understand context. Orchestration models enable the coordination of their actions. And Digital Job Descriptions provide the necessary framework to govern them securely.
At Brait, we conduct a diagnostic to map your agentic readiness
If you're exploring how to incorporate intelligent agents into your organization, we can help you build a solid foundation to do so scalably and securely. We'll guide you through your company's needs, its documented processes, and governance models for a meaningful new generation of AI-driven automation.
We evaluate all sensitive areas, process documentation, centralized data, governance, and system connectivity, such as integration with SAP, and provide you with a detailed roadmap. Contact us now!
