An AI agent architecture has seven core components: goal definition, perception and input processing, memory, reasoning and planning, tool execution and action, orchestration and coordination, and feedback and observability. Together, these layers define how an agent takes in information, decides what to do, acts across your systems, and improves over time. Get the architecture right and your agents stay reliable, secure, and governable. Get it wrong and they stall, hallucinate, or act outside their bounds.
An AI agent is software that pursues a goal on its own by planning steps, calling tools, and adapting based on results. What makes it dependable is not the model alone. The architecture around the model determines reliability, security, and governance, because it decides which data the agent can see, which actions it can take, and how you audit what it did.
Architecture matters more in the enterprise than in a consumer setting. A consumer agent that books a dinner reservation can fail quietly. An enterprise agent that resolves a support ticket, updates a CRM record, or files an expense touches permissioned data and takes real actions, so a weak layer becomes a security or compliance risk rather than a minor annoyance. The seven components exist to make each of those points controllable.
This is also what separates an agent from a simpler assistant. An AI agent plans across many steps and takes action, while a single-turn tool just answers. The sections below walk through each of the seven components, how they connect, and how Glean delivers each layer on one governed platform.
How AI agent architecture differs from a chatbot
An AI agent plans across multiple steps, holds memory, calls tools, and takes action, then checks its own work. A chatbot answers one question at a time and stops there. The difference is agency: an agent can break a request like "resolve this support ticket" into a plan, gather context, draft a reply, and close the ticket, while a single-turn responder can only tell you how to do it.
That gap shapes how you build. A chatbot needs a good model and a prompt. An agent needs every layer in this article, because planning, memory, and action each introduce new points where the system can fail or overstep. A wrong answer is easy to ignore. A wrong action, like closing the wrong ticket or emailing the wrong customer, has consequences, which is why agents demand the goal limits, permissions, and audit trails that a single-turn responder never touches.
Glean reflects this split in its product. Glean Assistant is a conversational interface that gives grounded, cited answers from your company's knowledge. Glean Agents plan, adapt, and act with enterprise context and governance. Both run on the same system of context, so an answer and an action draw from the same permission-aware data.
Goal definition
Goal definition anchors an AI agent's purpose. It sets what the agent is trying to achieve, how success is measured, and when it should stop. Clear goals keep agents inside approved boundaries, respect compliance constraints, and produce outcomes that match business intent.
A strong goal definition layer includes the task scope, performance priorities, and termination rules such as iteration caps, token budgets, or confidence thresholds. Take support-ticket triage: you might cap the agent at three research steps, set a token budget so it cannot loop indefinitely, and set a confidence threshold below which it escalates to a human instead of replying. Those limits turn an open-ended "help the customer" into a bounded task with a clear exit. When objectives are ambiguous, safety defaults and escalation paths prevent inappropriate behavior and make agent decisions auditable and predictable.
With Glean Agents, you define agent objectives and guardrails and govern them through Agent Governance, which enforces the scope, permissions, and escalation rules an agent runs under.
Perception and input processing
The perception and input layer is the sensory system of an AI agent. It collects and normalizes inputs from channels like email, chat, APIs, or enterprise data systems. Before any reasoning happens, the data must be validated and sanitized for security and relevance.
Input sanitization inspects and modifies incoming data to prevent malicious activity or corruption. This step defends against prompt injection or denial-of-service attempts that could compromise reliability. A well-designed input layer also enables multi-modal awareness, processing text, structured records, voice transcriptions, or images as unified context for decision-making.
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<th class="rich-text-table_header" scope="col">Input Source</th>
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<th class="rich-text-table_header" scope="col">Validation Layer</th>
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<td class="rich-text-table_cell">Slack / Chat</td>
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<td class="rich-text-table_cell">Sanitization, Entity Parsing</td>
<td class="rich-text-table_cell">Short-term memory</td>
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<td class="rich-text-table_cell">API Feeds</td>
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<td class="rich-text-table_cell">Persistent memory</td>
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<td class="rich-text-table_cell">Uploaded Files</td>
<td class="rich-text-table_cell">Documents / Media</td>
<td class="rich-text-table_cell">Virus Scan, Content Extraction</td>
<td class="rich-text-table_cell">Vector database</td>
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By securely ingesting multi-format data, agents build a reliable foundation for reasoning and memory. Glean unifies this perception pipeline across 100-plus connectors and the Enterprise Graph, which ingests content, activity, and identity data and scopes every input to each user's existing permissions.
Memory systems
Memory lets AI agents keep context and learn from past interactions. It splits into short-term working memory, used for immediate context, and long-term persistent memory, stored in external databases or vector stores for deep recall.
Working memory supports conversational continuity and dynamic task execution, while persistent memory enables richer personalization and long-range understanding. Many enterprises adopt stateless AI calls, storing all context externally to keep control and transparency. A hybrid model balances speed with cost: short-term caches for responsiveness, paired with vector databases for scalable retrieval.
Vector recall alone has a real limit. It matches on similarity but misses the relationships between people, content, and activity, so it can surface a document that reads relevant but is outdated or written by the wrong team. When an agent asks "what is our current pricing policy," similarity search may return an old deck instead of the approved page, because both read alike. Glean's Enterprise Graph and Personal Graph add that relationship layer, giving agents recall that weighs authority, recency, and the people connected to each document, and stays scoped to what the user is permissioned to see.
Reasoning and planning
The reasoning engine is where an AI agent decomposes goals, weighs available information, and picks the best sequence of actions. It turns an abstract task into a structured plan through deliberate steps and iterative logic.
Common reasoning patterns include rule-based systems for deterministic control, chain-of-thought strategies for structured deliberation, tree-of-thought approaches for exploring alternatives, and stochastic methods like Monte Carlo simulations for probabilistic optimization. In enterprise settings, hybrid reactive-deliberative models often balance agility and reliability best.
Reasoning strategyStrengthsLimitationsRule-basedPredictable, transparentRigid, hard to scaleChain-of-thoughtStrong for linear logicProne to overthinkingTree-of-thoughtExplores multiple pathsComputationally costlyMonte CarloHandles uncertaintyRequires large samples
Glean runs its own agentic reasoning on the Agentic Engine, which moves through five steps: search, reflect, plan, execute, and respond. At the reflect step, the agent assesses its confidence in an initial answer and decides whether to stop at search or continue down the agentic path, which keeps simple questions fast and reserves multi-step reasoning for hard ones. Early results from this architecture showed a 24% lift in the relevance of responses and actions. You can read the full breakdown in agentic reasoning: the future of Work AI.
Tool execution and action
After planning, an agent has to act, turning reasoning into results. The tool execution or action layer does this by invoking APIs, triggering workflows, or running commands under controlled conditions.
Enterprise agents connect to a growing set of tools such as CRM systems, ticketing platforms, and analytics dashboards, and they must do so safely. Secure tool routing uses schema validation, least-privilege permissions, and sandbox environments to control execution risk. This practice, often called action sandboxing, guards against misuse or unintended side effects.
Action typeExample toolSafety mechanismAPI callInternal HR systemAuthentication, rate limitsDatabase updateInventory DBSchema enforcement, write locksWorkflow triggerAutomation platformSandboxed execution
Post-action results feed back into the agent's memory, supporting iterative planning and transparent auditability. Glean Agents run tools with permission-aware execution, enforcing permissions upstream of the LLM so an agent can only act on data and systems the user is already allowed to reach.
Orchestration and coordination
At scale, multiple agents have to cooperate across tasks and domains. The orchestration and coordination layer keeps that collaboration structured, efficient, and resilient.
Two patterns dominate. In the supervisor-worker model, a supervisor agent delegates subtasks to specialized workers, so a customer issue can route to a billing agent, a technical agent, and an account agent in parallel, then the supervisor merges their results into one response. A sequential pipeline instead runs steps in a fixed order, where each step's output feeds the next, which fits a document-review flow that extracts, summarizes, and then routes for approval. Supervisor-worker suits branching work with several specialties, while pipelines suit predictable, linear processes. Resilience matters in both: orchestration layers need retry logic, fallback supervisors, and circuit breakers to keep one failed step from cascading across the workflow.
Glean Agent Orchestration coordinates agents across your systems with shared context and governance, so every agent in a workflow draws on the same permission-aware knowledge and operates under the same controls.
Feedback and observability
Feedback and observability systems close the loop, letting agents improve while staying auditable. This layer tracks every decision, state change, and tool call, creating a transparent history for compliance and debugging.
Observability in AI agents means built-in mechanisms for collecting, tracking, and analyzing agent decisions to support debugging, compliance, and continuous improvement. Paired with feedback loops, where agents learn from results and refine their planning, these systems raise performance and reliability. Essential features include automated error detection, human-in-the-loop oversight, and AI audit logging that aligns with regulations like the EU AI Act.
Automated evaluation is where this gets hard, and Glean has proprietary data on it. Grading agent output by hand does not scale once you run thousands of queries a day, but many teams assume human graders are far more accurate than an AI evaluator. When Glean tested its LLM-based evaluation against human graders, the AI agreed with graders 74% of the time, close to the 79% agreement humans reached with each other. That near-human accuracy lets Glean grade both retrieval and generation at scale and catch regressions before they reach users. Glean pairs this LLM-based evaluation with Agent Governance for audit trails and human-in-the-loop oversight, so you can monitor, audit, and trust what your agents do.
How the seven components work together
The seven components form a single loop. Goal definition sets the objective and stop conditions. Perception takes in and sanitizes the relevant inputs. Memory supplies past context and stores new context as it arrives. Reasoning and planning turn the goal and context into a step-by-step plan. Tool execution carries out each step and writes results back to memory. Orchestration coordinates the agents and tools involved when a task spans several specialties. Feedback and observability record every decision and evaluate the outcome, then route what they learn back into memory so the next run starts smarter. The loop repeats until the goal is met or a termination rule fires.
A labeled component diagram fits naturally here, mapping the flow from goal through perception, memory, reasoning, action, and orchestration to the feedback loop. The layers are only as strong as their weakest link: a sharp reasoning engine still fails if perception feeds it stale data, and safe actions mean little without the goal limits that decide when to stop. Glean assembles these seven layers on one governed system of context, so goals, data, actions, and audit trails share the same permission model instead of being stitched together across separate tools.
Frequently asked questions
What are the core components of an AI agent architecture?
The core components are goal definition, perception and input processing, memory, reasoning and planning, tool execution and action, orchestration and coordination, and feedback and observability. Each layer handles one part of how an agent takes in information, decides, acts, and improves.
How is an AI agent architecture different from a chatbot?
An AI agent plans across steps, holds memory, calls tools, and takes action, then checks its own work, while a chatbot answers a single turn and stops. Glean reflects this in its product: Glean Assistant gives grounded, cited answers, and Glean Agents plan and act with governance.
Why is memory important in AI agents, and what types exist?
Memory lets agents keep context and personalize outputs, using short-term working memory for immediate tasks and long-term persistent memory for deep recall. Glean's Enterprise Graph and Personal Graph make that recall relationship-aware and permission-governed rather than based on similarity alone.
How do tools and actions enable autonomy in AI agents?
Tools and actions let agents interact with external systems, run tasks, and execute decisions to complete work end to end. Glean Agents run these actions with permission-aware execution, enforcing permissions upstream of the LLM so agents only act on data the user can access.
What role does feedback and observability play in improving AI agents?
Feedback and observability let agents learn from outcomes, correct errors, and stay auditable through logged decisions and evaluation. Glean's LLM-based evaluation reached 74% agreement with human graders, near the 79% humans reached with each other, and pairs with Agent Governance for audit trails.
Request a demo to explore how Glean and AI can transform your workplace: https://www.glean.com/get-a-demo
