The Canonical Structure of Enterprise AI Agents
Agent demos collapse under production load. Learn the architectural components that separate proof of concept from production-grade enterprise AI agents.
The agent that dazzled in a demo collapses the moment it touches five live enterprise systems simultaneously. The difference between a proof of concept and production-grade agent is not the model: it is the architecture underneath. Understanding that architecture is the first step toward identifying where your readiness gaps sit.
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What Are Enterprise AI Agents?
Enterprise AI agents represent a fundamental departure from the AI tools most organizations have deployed over the past decade. Where traditional AI systems wait for instructions and return outputs, enterprise AI agents perceive their environment, reason about goals, plan multi-step approaches, and execute actions; often with minimal human direction.
Beyond Chatbots: The Agentic Difference
The distinction matters more than most teams initially realize. A chatbot responds to a single query. An enterprise AI agent pursues a multi-step goal across systems, retaining memory of what happened three interactions ago and adjusting its approach based on what it learned. As Sema4.ai describes it, enterprise AI agents “act as independent decision-makers that can perceive their environment, retain memory of past interactions, and take purposeful actions to meet specific business objectives” (Sema4.ai.
At the core sits what practitioners call the capability loop: perception, reasoning, planning, action, and reflection. The agent observes its environment; incoming data, system states, user requests. Reasoning Engines, typically powered by Large Language Models (LLMs), serve as the Cognitive Architecture that interprets goals and evaluates options. The agent plans a sequence of actions, executes them through its Action/Execution Modules, and then reflects on the outcome to adjust future behavior.
What makes this enterprise-grade is the concept of structured autonomy. These agents do not operate with unlimited freedom. They work within enterprise-defined boundaries; specific authority levels, data access permissions, and governance guardrails that constrain what the agent can do without human approval (Kore.ai. Agentic AI in enterprise settings means Goal-Driven Behavior combined with adaptability: the agent can perceive when circumstances change and modify its approach accordingly, but only within the boundaries the organization has established.
The reasoning engine, typically an LLM, is what separates enterprise AI agents from earlier automation. It enables the agent to handle ambiguity, interpret natural language instructions, and generate novel action sequences rather than following rigid scripts. Memory across interactions means the agent builds context over time, meeting business objectives not just in a single session but across an entire engagement lifecycle.
The Canonical Four-Tier Architecture of Enterprise AI Agents
Before assessing where your organization’s agent capabilities stand, it helps to understand the reference architecture that most enterprise implementations share. The Four-Tier Architecture provides a consistent taxonomy for evaluating vendors, governing deployments, and identifying capability gaps.
The Four Tiers and Their Purpose
The canonical architecture organizes enterprise AI agent systems into four interconnected tiers:
- Engagement Tier: The user-facing layer where humans interact with the agent through natural language, dynamic UIs, and collaboration tools. This is where intent enters the system.
- Autonomous/Action Tier: The execution engine where Orchestration/Management Systems coordinate multi-step workflows, manage multi-agent coordination, and drive Action/Execution Modules across enterprise systems.
- AI/ML Layer: The intelligence layer housing the LLMs, governance models, and reasoning capabilities that power decision-making. The Model Training Process shapes baseline model behavior, while Inference-Time Reasoning handles novel situations at execution time.
- Data Tier: The knowledge foundation; memory systems, vector databases, knowledge graphs, and systems of record that ground every agent decision in enterprise reality.
The flow is top-down: a user engages the agent through the Engagement Tier, which routes intent to the Autonomous/Action Tier. That tier calls on the AI/ML Layer for reasoning and the Data Tier for context, then executes the resulting plan.
This taxonomy is not theoretical. Salesforce’s Agentic Enterprise architecture maps directly to these tiers, organizing capabilities around engagement, action, intelligence, and data Agentic Enterprise (Salesforce). Kellton’s enterprise Agentic Architecture describes a similar three-tier model; engagement, capabilities (combining action and intelligence), and data Agentic Architecture (Kellton). The convergence across vendors validates a canonical pattern.
Why does this matter? Because a consistent taxonomy enables organizations to assess where they are strong, where gaps exist, and where investment creates the most impact; tier by tier. It also makes vendor comparison rigorous rather than anecdotal, and maps governance requirements to specific architectural layers rather than treating security and compliance as afterthoughts.
Engagement Tier: How Enterprise AI Agents Interface with Users
The Engagement Tier is where every agent interaction begins; and where most user trust is won or lost. This tier determines how naturally humans can communicate intent, how transparently the agent surfaces its reasoning, and how seamlessly the agent fits into existing work patterns.
Dynamic Interfaces and Collaboration Integration
At its core, the Engagement Tier handles natural language as the primary interaction modality. Users describe what they need in plain language, and the tier translates that intent into structured requests the Autonomous/Action Tier can execute. But modern engagement goes well beyond a text box.
Dynamic UI Generation Services allow agents to modify interface components in real-time based on user context. If the agent determines that a visual comparison table would best communicate its findings, it generates one on the fly rather than forcing the user to interpret raw text. Salesforce describes these as services that “allow an AI agent to generate or modify UI components in real-time based on user context” (Salesforce.
AI-Native UI Frameworks are the front-end infrastructure designed specifically for the probabilistic nature of agent outputs. Traditional UIs expect deterministic data: a number is a number. Agent UIs need to handle streaming text responses, confidence indicators, and outputs that evolve as the agent reasons. These frameworks manage the inherent uncertainty of AI-generated content without confusing the user.
The real enterprise value emerges through Agent-Infused Systems of Engagement; existing productivity and Collaboration tools like Microsoft Teams, Slack, and CRM platforms that incorporate agent capabilities directly. Rather than forcing users to learn a new interface, agents meet them where they already work. Human-in-the-Loop Workflows are built into this tier, allowing the agent to surface decisions that require human judgment before continuing execution.
The Engagement Tier’s final responsibility is routing. Once user intent is captured and clarified, the tier translates it into a structured request and passes it to the Autonomous/Action Tier. How well this handoff works determines whether the agent feels responsive or frustratingly slow. Multimodal Agent Models are increasingly relevant here, processing not just text but images, voice, and even video to understand what the user actually needs through APIs and multimodal interfaces.
Autonomous/Action Tier: Where Enterprise AI Agents Execute Work
This is the tier where promises become reality; or fall apart. The Autonomous/Action Tier is responsible for taking the intent captured by the Engagement Tier and executing complex, multi-step workflows across enterprise systems.
Levels of Autonomy and Orchestration Patterns
In my experience, the most useful way to assess this tier is through a four-level autonomy scale. AWS describes it as ranging from engineer-guided agents that follow predefined steps, through context-adapted agents that can adjust their approach, to fully autonomous agents that “proactively set goals, adapt to outcomes, and may even create or select their own tools” (AWS. Most enterprise deployments today sit at Levels 2-3, with full autonomy reserved for narrow, well-governed domains.
Durable Execution is the architectural pattern that separates production agents from demos. Long-running workflows, the kind that span hours or days, touch multiple systems, and inevitably encounter failures, need to survive interruptions and recover gracefully. Without durable execution, a network timeout at step 47 of a 50-step workflow means starting over.
The Workflow Orchestration Process increasingly uses graph-based approaches for multi-agent coordination, enabling both sequential and parallel task flows. Orchestration/Management Systems handle the critical work of dependency management, failure recovery, and output coherence when multiple agents operate in concert: one handling data extraction while another runs analysis and a third prepares the report.
Human-in-the-Loop Workflows appear at configurable thresholds throughout this tier. High-impact decisions, approving a purchase order above a certain amount, modifying a customer contract, or escalating a security alert, trigger human review before the agent proceeds. The key insight is that these thresholds should be configurable by the organization, not hardcoded by the vendor. Agentic AI Governance requires aligning review depth with the autonomy level of each agent Agentic AI Governance (Palo Alto Networks).
This tier connects to enterprise systems through APIs, Robotic Process Automation (RPA) for legacy systems that lack modern interfaces, and workflow engines that manage the sequencing. Action/Execution Modules within this tier handle the actual system interactions; writing data, triggering processes, calling external services. The Active Orchestrator pattern, executing across enterprise systems with governance controls at each step, is what makes enterprise agents fundamentally different from standalone AI tools.
AI/ML Layer: Models and Governance Inside Enterprise AI Agents
The AI/ML Layer is where the intelligence lives; but in enterprise settings, intelligence without governance is a liability. This layer houses both the Reasoning Engines that make agents useful and the controls that make them safe.
Reasoning, Knowledge, and Risk Controls
Large Language Models (LLMs) serve as the core reasoning engine, interpreting goals, evaluating options, and planning multi-step workflows. Databricks describes the implementation as “orchestrating three complementary layers: Large Language Models (LLMs), machine learning (ML) algorithms and autonomous agent control” Large Language Models (Databricks). The Cognitive Architecture of this layer integrates multiple reasoning capabilities: Inference-Time Reasoning enables agents to think through problems at execution time rather than relying solely on training-time patterns, while the Model Training Process shapes the foundational behavior that agents build upon. Together, these create a system capable of handling both familiar patterns and novel situations.
The Agentic Knowledge Layer sits within this tier, acting as what Alation describes as a “metadata lakehouse that connects with 100+ systems across cloud warehouses, BI platforms, and ML environments” Agentic Knowledge Layer (Alation). This layer ensures AI agents access contextualized enterprise knowledge: not just raw data, but data with meaning, lineage, and governance attached.
Governance at the model level means embedding role-based access, data permissions, and audit trails directly at inference time. Canonical Models and Risk Controls function as governance primitives; standardized definitions and constraints that every agent decision must respect. Alation’s operating model framework describes this as “embedding governance, critical data definitions, canonical models, and risk controls into the foundation on which AI systems run” (Alation.
The critical distinction here is between training-time alignment and live execution governance. Training-time alignment shapes how the model behaves in general; live execution governance addresses what happens when an agent operates inside real environments where actions affect real workflows. As Palo Alto Networks notes, “training-time alignment alone doesn’t address execution risk” (Palo Alto Networks. Machine-legible process models within this layer enable both deterministic workflows (following strict rules) and dynamic work (adapting based on context) to coexist: a requirement for enterprise environments where some processes must be exact and others need flexibility. AI/ML Engineers are the practitioners who configure, fine-tune, and govern these models within enterprise constraints.
Data Tier: Knowledge, Memory, and State in Enterprise AI Agents
The Data Tier is the foundation everything else rests on; and the layer where most enterprise agent implementations quietly fail. Without governed, accessible, high-quality data, even the most sophisticated reasoning engine produces unreliable results.
Memory Architecture and Enterprise Data Integration
Enterprise AI agents require three distinct types of memory. Short-term memory handles Session/State Management, typically implemented through fast storage like Redis, keeping track of the current conversation, active workflow state, and immediate context. Long-term memory combines Memory Systems (e.g., Vector Databases) for semantic search with Enterprise Knowledge Graphs for structured fact retrieval. Procedural memory, sometimes called an AI Asset Registry, stores system prompts and learned behavioral adjustments. As one practitioner notes, this ensures “context is not lost as tasks are handed off between agents and that the system as a whole can learn over time” (Unstructured.io.
Vector Search & Retrieval-Augmented Generation (RAG) serves as the primary mechanism for grounding agent decisions in live enterprise data. Rather than relying solely on what the LLM learned during training, RAG pulls current information from enterprise sources at decision time; ensuring the agent works with facts, not memories of facts.
Enterprise Knowledge Graphs provide structured memory that goes beyond what vector search alone can offer. Knowledge graphs “provide structured memory, real-time fact retrieval, and rules that ground AI systems in verifiable business data” (Superblocks. They encode relationships between entities, customers, products, policies, processes, that the agent needs to reason about accurately.
Here is where most implementations stumble. Production-grade agents routinely orchestrate data from 5, 10, or even 15+ enterprise systems simultaneously. As Informatica observes, “while chatbots and basic RAG systems rely on one curated knowledge base, production-grade agents routinely orchestrate data from 5, 10, or even 15+ enterprise systems at once” (Informatica. The organizations that succeed treat their Tiered Memory Architecture as a first-class concern; tiered, observable, governed, and aligned to enterprise risk frameworks. Systems of Record provide the authoritative data sources, Data Intelligence ensures meaningful interpretation, and a rigorous Data Preparation Process ensures that data is clean, current, and accessible before agents attempt to use it. Every agent decision must be traceable to the data inputs and prior state that influenced it: an audit trail requirement that separates enterprise systems from unmanaged automation.
Enterprise AI Agents vs. RPA, Copilots, and Traditional Automation
When organizations evaluate enterprise AI agents, the first question is often: “How is this different from what we already have?” The answer determines whether you invest in the right tool for the right problem; or spend months deploying a solution that does not match your actual need.
Mapping the Automation Landscape
Robotic Process Automation (RPA) is rule-based, deterministic, and brittle to change. RPA bots execute predefined sequences; clicking through screens, copying data between fields, following scripted workflows. They excel at high-volume, repetitive tasks where the process never varies. But introduce an unexpected format, a new field, or an edge case, and the bot breaks.
AI Copilots, Microsoft Copilot being the most visible example, represent a significant step forward. Copilots reason over data, identify patterns, and recommend actions. The critical limitation: humans still execute those recommendations manually. As one enterprise guide notes, “Microsoft Copilot reasons over Microsoft 365 data and recommends actions; humans execute those recommendations manually” Microsoft Copilot (Ampcome).
Enterprise AI Agents close that gap. They govern and execute actions autonomously across enterprise systems, combining Goal-Driven Behavior, multi-step reasoning, contextual adaptation, and tool orchestration. An agent does not just recommend closing a support ticket: it resolves the underlying issue across CRM, billing, and logistics systems, then closes the ticket. Decision-Making Autonomy is the core differentiator: agents make and act on decisions within governance boundaries, while copilots only advise.
Traditional Automation, scripted workflows, scheduled jobs, ETL pipelines, operates without any contextual reasoning. It does exactly what it was programmed to do, regardless of whether circumstances have changed.
| Capability | RPA | AI Copilots | Enterprise AI Agents | Traditional Automation |
|---|---|---|---|---|
| Decision-Making Autonomy | None, follows scripts | Recommends, humans decide | Autonomous within governance | None, executes scripts |
| Contextual Adaptation | No | Partial | Yes, adapts to changing conditions | No |
| Multi-Step Reasoning | Sequential only | Single-turn analysis | Multi-step planning and execution | Predefined sequences |
| Cross-System Orchestration | Limited | Microsoft ecosystem | Full enterprise scope | Point-to-point |
The practical guidance: use RPA for high-volume deterministic tasks where the process is stable. Use copilots when human judgment remains essential but needs AI-powered context. Deploy enterprise AI agents for complex, context-dependent workflows where the agent needs to reason across systems and adapt to what it finds. Single-Agent Systems handle contained domains well, while Multi-Agent Systems and Hybrid Architectures address workflows that span organizational boundaries.
How to Deploy Enterprise AI Agents: Implementation Essentials
Deploying enterprise AI agents is not a technology project: it is an organizational transformation. The teams that succeed treat deployment as a four-pillar discipline rather than a technical installation.
The AI Agent Operating Model
The AI Agent Operating Model organizes deployment around four pillars: strategy, governance, opportunity discovery, and agile delivery. Each pillar must be addressed from day one, not bolted on after the technology is running.
Opportunity Discovery Methodology comes first. Before building anything, identify and prioritize use cases by impact and feasibility. The organizations that skip this step typically deploy agents for impressive-sounding but low-impact tasks, then struggle to justify continued investment. Assess which workflows have the highest combination of complexity, frequency, and business value: those are your deployment targets.
Data Preparation Process is consistently the highest-leverage implementation barrier. In my experience, organizations underestimate this by an order of magnitude. The agent can only be as good as the data it accesses. If your customer records are inconsistent across systems, your product catalog is outdated, or your process documentation lives in people’s heads, the agent will produce unreliable results regardless of how sophisticated the model is.
Tool Integration Process connects the agent to enterprise systems. Model Context Protocol (MCP) provides a standardized approach for agent-to-system communication, while APIs and managed connectors handle specific platform integrations. The goal is giving the agent reliable access to the systems it needs without creating a maintenance nightmare of custom integrations.
Governance Model must be embedded from day one; roles, ethics guidelines, security policies, and compliance requirements that govern what the agent can and cannot do. Organizations that defer governance until after deployment invariably face costly retrofitting.
Agile Delivery Methodology applies to agent deployment just as it does to software: pilot, validate, scale. Start with a constrained use case, measure results against clear success criteria, iterate based on what you learn, then expand scope. Waterfall deployment, designing the complete system before testing anything, fails for agents just as reliably as it fails for software. Cross-functional collaboration between business stakeholders, data teams, and technology teams is not optional; it is the mechanism that ensures the agent solves real problems rather than interesting technical challenges.
Why Enterprise AI Agents Fail: Security, Hallucinations, and Scalability Challenges
Understanding failure modes before deployment is far more valuable than discovering them in production. The organizations that anticipate these challenges build defenses proactively rather than reactively.
- Agentic AI Security Challenges: Prompt Injection attacks manipulate agent behavior by embedding malicious instructions in seemingly normal inputs. Sensitive data leakage occurs when agents with broad system access surface information to unauthorized users. The attack surface expands with each system the agent connects to. A Data Governance Officer should own the policies that control data access boundaries across agent interactions.
- Hallucination Rate: Agents fabricate information during summarization and generation tasks. In an enterprise context, a hallucinated contract clause or an invented compliance requirement can have material consequences. The Hallucination Rate must be measured continuously, not assumed to be acceptable based on benchmark tests.
- Scalability Challenges: Moving from one agent handling one workflow to multiple agents orchestrating across 5-15+ enterprise systems introduces state management complexity, coordination overhead, and failure cascading that most pilot architectures cannot handle. Orchestration/Management Systems must be production-grade from the start, not retrofitted after scaling begins.
- Data readiness: Most pilots fail on data quality, not model capability. If the enterprise data is inconsistent, siloed, or stale, the agent’s reasoning, however sophisticated, produces unreliable outputs.
- Governance gaps: Training-time alignment shapes general behavior, but live execution risk requires separate governance. An agent that behaves well in testing may take unexpected actions when encountering production edge cases that training data did not cover.
- Human oversight deficit: Agents operating in live environments without configurable thresholds for human review create uncontrolled risk. The Exception Handling Rate and Recovery Rate should be tracked from the first deployment, not after an incident.
- Legacy integration friction: Agentic AI Security Challenges multiply when agents must interact with legacy systems that offer inconsistent data formats, limited APIs, or no audit trail capability. Technical Debt in enterprise systems becomes the agent’s technical debt.
Enterprise AI Agent Use Cases: Where the Architecture Delivers ROI
The canonical Four-Tier Architecture is not theoretical: it enables specific, measurable outcomes across enterprise functions. The use cases that deliver the highest Return on Investment share a common pattern: complex, multi-system workflows where contextual reasoning replaces manual coordination.
Industry Applications
Customer Service: Task Automation Agents handle end-to-end issue resolution, pulling context from CRM, order management, and knowledge bases to resolve problems without escalation. Customer Service Representatives benefit when the Containment Rate, the percentage of issues resolved without human handoff, improves significantly over traditional routing.
Finance: Enterprise AI agents tackle reconciliation automation, accelerated book closing, and exceptions management. Finance Analysts benefit most when agents handle the high-volume pattern matching, invoice discrepancies, supply chain error prevention, compliance checks, freeing human judgment for genuinely ambiguous cases.
HR: Agents personalize employee growth paths by synthesizing performance data, available training, and internal mobility opportunities. HR Specialists use Enterprise Search and Knowledge Agents to surface relevant policy information instantly rather than navigating multiple systems.
IT and Security: CI/CD test generation, threat detection, and automated incident response represent high-value use cases. Cybersecurity Specialists leverage agents that can correlate alerts across monitoring systems, assess severity using historical patterns, and execute initial containment steps before human review.
Manufacturing: Product design optimization, inventory reordering based on demand signals, and operations error resolution all benefit from agents that can reason across sensor data, ERP systems, and quality records simultaneously. Manufacturing Engineers find the most value when agents handle the cross-system coordination that previously required manual oversight across multiple dashboards.
In each case, the canonical architecture, Engagement Tier for user interaction, Autonomous/Action Tier for execution, AI/ML Layer for reasoning, Data Tier for grounded context, provides the production-grade foundation that separates reliable deployment from fragile demos.
Measuring Enterprise AI Agent Performance: Metrics, Observability, and ROI
Deploying an enterprise AI agent without a measurement framework is like launching a product without analytics: you will not know whether it is working until something visibly breaks. The challenge is identifying which metrics actually indicate business value versus which are vanity indicators.
Four Dimensions of Agent Measurement
Performance metrics: Task Success Rate measures whether the agent completes its assigned objective. Deflection Rate tracks how many requests the agent resolves without human escalation. Together, these two production KPIs tell you whether the agent is doing its job.
Quality metrics: Hallucination Rate and Tool/Action Selection Accuracy serve as quality gates. An agent that completes tasks but fabricates information along the way is worse than one that escalates to a human. These metrics require ongoing monitoring, not one-time evaluation.
Cost metrics: LLM Cost per Task, Latency Per Agent Loop, and Cost per Interaction determine whether the agent delivers positive Return on Investment at scale. An agent that resolves issues brilliantly but costs three times what a human resolution costs is not delivering business value.
Autonomy and maturity metrics: Autonomy Level tracks the degree of independence the agent exercises. Decision Turn Count measures how many reasoning steps the agent takes per task: a proxy for complexity handled. Context Utilization Score evaluates how effectively the agent applies memory and historical context to current decisions, distinguishing agents that truly learn from those that treat every interaction as new.
Observability is the non-negotiable requirement that ties all dimensions together. Every agent decision must be traceable to its data inputs and reasoning steps. Without observability, you cannot diagnose failures, optimize performance, or demonstrate compliance. Organizations that treat observability as an afterthought find themselves unable to explain why the agent made a specific decision; which becomes a governance crisis the first time a decision goes wrong.
Enterprise AI Agent Maturity: From Pilot to Production Architecture
Scaling enterprise AI agents from a successful pilot to production architecture is where organizational capability matters more than technology sophistication. The maturity path is predictable, but each stage demands different capabilities from the organization.
The Maturity Stages
Ad-hoc experimentation: Teams explore agent capabilities through informal proofs of concept. Governance is minimal, use cases are opportunistic, and success is measured anecdotally. Most organizations are here or just beyond.
Structured piloting: Formal pilot programs with defined success criteria, bounded scope, and lightweight governance. The Agile Delivery Methodology applies; iterate quickly, measure rigorously, and decide whether to scale based on evidence rather than enthusiasm.
Validated scaling: Proven pilots expand to additional use cases and user groups. This stage demands production-grade infrastructure: Durable Execution architecture, formal governance frameworks, and cross-functional operating models. The AI Agent Operating Model becomes essential; strategy, governance, opportunity discovery, and delivery must all scale together.
Production architecture: Full enterprise deployment with mature governance, monitoring, and continuous improvement. OpenAI’s own internal data agent, serving 3,500 users across 600 petabytes of data, illustrates what production maturity looks like (OpenAI.
Task Sequencing Maturity
Within the Autonomous/Action Tier, the Task Sequencing Process follows its own progression: engineer-defined sequences (every step prescribed), context-adapted sequences (the agent adjusts order based on conditions), and agent-generated sequences (the agent creates novel workflows for novel situations). Most production deployments today operate at the context-adapted level.
The role of Durable Execution architecture cannot be overstated: it is the production-readiness gating requirement. Without it, agents that work in controlled environments fail unpredictably under real-world conditions.
Change Management Specialists play a critical role at every stage. Training employees to supervise and collaborate with AI agents, rather than compete with them or blindly trust them, determines whether the organization captures the value the technology enables. Human-in-the-Loop Workflows evolve from nearly-constant oversight during pilots to targeted intervention at production scale, with governance frameworks evolving in parallel.
In 2026, leading enterprises have achieved structured piloting and validated scaling for specific use cases, with production architecture emerging in domains like customer service and IT operations. Broader enterprise-wide production maturity remains ahead for most organizations; which makes identifying where to invest now the highest-leverage decision on the table.
Summary
Enterprise AI agents are defined by their canonical Four-Tier Architecture: Engagement, Autonomous/Action, AI/ML, and Data tiers working together to deliver structured autonomy within enterprise boundaries. The Cognitive Architecture that powers these agents, from Reasoning Engines within the AI/ML Layer to Orchestration/Management Systems coordinating Action/Execution Modules, requires deliberate design across every tier. The difference between successful deployments and failed pilots consistently comes down to data readiness, embedded governance from day one, and production-grade infrastructure; particularly Durable Execution and observable memory architecture. Organizations that assess their maturity across each tier, measure performance through meaningful metrics, and scale through disciplined piloting position themselves to capture the value that enterprise AI agents represent; while those that skip the architectural foundations continue cycling through demos that never reach production.
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Where this leads next
The structure set out here is what an enterprise agent is made of, component by component. Those components sit in named layers, engagement, autonomous action and the model layer beneath them, and that layering is defined as a framework in its own right:
Three-Tier Agentic AI Architecture Framework (AI Architecture)