Agentic AI is not one design but a family of them, and the architecture you choose should follow the shape of the task. A single agent that reasons and calls tools toward a user-defined goal suits narrow, well-defined workflows. When a problem can be decomposed into interdependent sub-tasks, a vertical (hierarchical) arrangement with a supervising orchestrator agent makes sense. When work is exploratory and benefits from peers sharing ideas, a collaborative multi-agent system fits best. This guide compares the three patterns and maps each onto the three layers of an enterprise agentic platform: orchestration and management, data connectivity, and observability and governance.
Understanding Agentic AI: Beyond Simple Bots
What separates an agent from a chatbot or assistant is autonomy of execution. Akka's enterprise guide describes agents as systems "focused on solving a user defined goal, using reasoning and tools to reach a goal" — perceiving, planning, and acting rather than merely responding. GeeksforGeeks similarly characterizes agents as combining "perception, reasoning, learning and decision-making abilities," handling complex tasks by adapting as conditions change.
That behavioral shift has an architectural consequence. Bain's analysis of agentic platforms, authored by Eric Sheng, Roger Zhu, Brendan O'Rourke, Dale Pedzinski and Kevin Zhang, argues that agentic AI "demands a fundamentally new architecture built for connected, nondeterministic systems rather than isolated models." Most enterprise AI platforms, they observe, were built for simpler systems: single models serving narrow use cases behind static API endpoints, with orchestration logic living inside individual applications and governance "tacked on after deployment." Agents break those assumptions, because they act across systems, in unpredictable sequences, at machine speed.
The Three Essential Layers of an Enterprise Agentic AI Platform
Whatever agent pattern you deploy, the platform beneath it needs to provide three functions. The first is orchestration and management: the logic that sequences work, delegates tasks, and manages the lifecycle and planning of agents. In a single-agent system this is mostly sequencing tool calls; in multi-agent systems it expands to inter-agent communication, delegation, and state synchronization — a materially harder problem, and the reason the orchestration layer is where architectural complexity concentrates.
The second is data connectivity. Bain notes that in older enterprise platforms, data flowed into specific applications through fixed ETL pipelines. Agents instead need governed, connected access to enterprise data on demand, because a reasoning system that must act across systems cannot rely on data pre-staged for one application.
The third is observability and governance. Salesforce's agentic enterprise architecture documentation describes this layer as one that "monitors and manages the health and operational performance of agents and the entire system," generating insights for auditing, debugging, explainability, and cost and resource optimization. Salesforce flags a specific risk that makes this layer non-optional: "the risk of AI agents creating errors at machine speed." Its guidance is that observability must expand beyond infrastructure monitoring to the unpredictable behavior of autonomous agents, requiring "new kinds of telemetry and the ability to understand semantic correctness, not just technical performance."
