What changes when an AI system stops answering questions and starts carrying out work?
In early enterprise generative AI architectures, the model often sat at the center. Companies added prompts, retrieval, tools, and connectors around it. Enterprise AI agents create a different requirement because they can plan steps, use tools, work across systems, keep state, and take actions.
The model still supplies reasoning, but it no longer defines the operating environment. A production agent also needs a surrounding system that controls what information reaches the model, which capabilities are available, how state is preserved, which actions are permitted, and how failures are handled.
Microsoft now documents this separation in Copilot Studio: the model provides reasoning while a harness sits between the model and the agent configuration, managing model calls, components, results, and tools.
The architecture question for 2026 is no longer only “Which model should we use?” It is “What must exist around the model before an agent can operate safely inside the business?”
If the model supplies reasoning, what coordinates everything the agent needs to complete a real task?
That role is handled by the agent harness. It is the runtime and orchestration layer that organizes the model’s work. Microsoft’s Copilot Studio documentation shows that the harness affects planning, tool use, files, memory, and recovery when a step changes.
For practical design, these responsibilities can be grouped into four areas:
Skills are also becoming governed assets rather than instructions buried within a single agent. Google Agent Registry can register, version, and share standalone skills in Preview, while AWS AgentCore now evaluates whether an agent selected the right skill and followed its instructions. Tools define what an agent can call; skills increasingly define how a reusable task should be performed.
For a business, the value is consistency. Instead of embedding prompts, memory rules, connection logic, and controls inside every agent, common capabilities can be standardized around them. Once that layer exists, the next question is what business context the agent should receive.
What happens when an agent can access the right data but still does not understand what it means?
Retrieval solved only part of the problem. Context engineering addresses what retrieval cannot solve: interpreting information inside the right business meaning.
A customer, product, order, or revenue figure may mean different things across systems. An agent needs those differences expressed in a form it can use during a task.
That is why the context layer goes beyond document retrieval. It can combine operational data, metadata, governed business definitions, relationships, business rules, permissions, and trusted sources. Its job is to provide the smallest useful set of trusted context for the task.
Databricks’ Genie Ontology, currently in Public Preview, shows how this architecture is taking shape. Its documentation describes a business-aware context layer combining governed definitions with context inferred from existing data assets and usage. It can represent metric definitions, authoritative sources, and business rules while respecting existing permissions.
This changes the data foundation for enterprise AI agents. Business meaning should not remain scattered across dashboard formulas, undocumented processes, employee knowledge, and prompts. If each agent carries its own definitions, agents can interpret the same question differently even when they use the same data.
Trusted context, therefore, needs to become a shared enterprise capability, giving agents a common set of trusted business definitions.
Where does an agent run when its work lasts longer than one API request and involves tools, files, or other agents?
Traditional cloud applications often receive a request, perform compute, return a response, and release the environment. AI agent infrastructure supports a different pattern: multi-step work that may pause, resume, preserve files, maintain session state, execute code, wait for another system, or react to an event.
The harness coordinates the agent’s behavior. The execution runtime provides the environment in which that work runs.
AWS AgentCore documents isolated sessions for extended conversations and multi-step workflows. Its September updates also added persistent interactive shells within isolated microVM sessions, preserving working directories, environment variables, command history, and running processes across input changes.
The connection layer is also being standardized. Google Cloud’s Agent Registry provides a central catalog for agents, skills, endpoints, tools, and Model Context Protocol (MCP) servers. Its Agent Gateway can govern communication between agents, tools, APIs, and other agents.
This creates an important separation. The agent does not need every tool hard-coded into its own application. Tools can be exposed through governed interfaces, while other agents can advertise capabilities that are centrally discovered and controlled.
For businesses, the question shifts from “How do we connect this agent?” to “How do we create a reusable execution and connection layer for many agents?” Once that layer exists, identity and authority become the next design problem.
Once an agent can reach business systems, how does the organization decide what it is allowed to do?
Giving an agent access is not the same as giving it unlimited authority. One of the first control requirements is a distinct identity for the agent itself.
Microsoft automatically creates Microsoft Entra Agent IDs for new Copilot Studio agents, providing administrators with identities that can be governed, audited, and targeted with access policies. Google Cloud uses a unique cryptographic Agent Identity that works with its Registry and Gateway for authentication, authorization, and auditing.
Identity then connects to policy. Google’s Agent Gateway can enforce access rules when agents call tools, APIs, MCP servers, or other agents, restricting which destinations and actions are permitted. This allows permissions to be enforced while the agent is operating.
Orchestration completes the control layer. An agent may decide what should happen next, but sensitive transactions do not have to be executed directly by the model. Rule-based workflows, APIs, approval rules, and transaction systems can remain responsible for the final action.
The operating model becomes clearer: agents reason, governed services execute, policies limit authority, and people remain involved where approval is necessary. AI orchestration is therefore as much a control problem as an automation problem.
How do you know that an agent is working correctly when the application itself appears healthy?
Traditional monitoring can show that an API responded, a database was available, and infrastructure stayed online. None of those signals proves that an agent selected the right tool, used the correct context, followed the intended route, or completed the right task.
That is why AI agent observability must examine both agent behavior and infrastructure. Observability shows what the agent did. Evaluation determines whether that behavior was acceptable.
AWS AgentCore Evaluations supports built-in and custom evaluation of agent behavior, including task completion and tool use. It also supports online evaluation against live production traffic, as well as on-demand and batch evaluation. In September, AWS added skill-specific evaluators that check whether agents selected the right skill and followed its instructions.
This moves evaluation from a one-time test before deployment toward continuous measurement after deployment. Microsoft’s agent lifecycle also includes dedicated evaluation and monitoring capabilities as agents move from development into operation.
The business lesson is simple: reliability should not depend on a successful demo. It should be measured as models, tools, business rules, and data change. Once behavior can be measured, organizations can expand autonomy with clearer evidence rather than assuming that a more capable model automatically yields a more dependable system.
What should an organization prepare before enterprise agents move from isolated tasks into day-to-day operations?
The architecture around the agent now matters as much as the intelligence inside it. Context, execution, identity, orchestration, and evaluation determine whether an agent can move from isolated capability to dependable business operation.
Organizations do not need to build every architectural layer before deploying their first agent. They do need to avoid treating each agent as a standalone application with its own definitions, credentials, tools, and controls.
For 2026 and beyond, the stronger approach is to design reusable foundations that support multiple agents and models. That makes the architecture easier to govern and reduces unnecessary dependence on any single model or agent implementation.
Futran Solutions helps organizations assess how their data, cloud, automation, integration, and governance foundations can support production-ready enterprise AI agents.
Article by: Vivek Kumar Upadhyay
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