Core concepts of AI agents

AI agents have evolved from passive chatbots to autonomous systems capable of reasoning, using corporate tools, and executing complex workflows.

To tap their huge potential—and move from experimental use cases and prototypes to robust, enterprise-grade systems that drive measurable ROI—it helps to understand the building blocks. Here, we break down the core concepts of AI agents, including:

Models: The reasoning engine that the agent uses to think with
Grounding: The knowledge base, which becomes the mechanism for factual accuracy and knowledge retrieval
Tools: Defined capabilities for performing tasks, which determine what an agent can do
Data architecture: Where the agent stores its memory and data
Orchestration: How the agent plans and connects all the parts in a multi-step task
Runtime: Where the agent lives and executes at scale

Models

Think of the model as your agent’s brain. It reads and understands your requests, figures out what needs to happen, and generates smart responses.

Choosing the right model is a matter of balancing capability, speed, and cost for your use case. The goal isn’t to maximize raw power, but to optimize for efficiency. The most common mistake is over-investing in capability when a use case doesn’t need it, leading to inefficient spending and slower performance.

Robust cognitive architectures employ multiple specialized agents that dynamically select the leanest model for their specific sub-task. It’s like having a team of specialists on hand—with jobs intelligently routed to different specialists depending on the task. For example, a powerful model is reserved for the heavy lifting of complex planning and reasoning, while simpler, high-volume tasks like classifying user intent are routed to a faster, more cost-effective model. This dynamic model routing is key to optimizing both performance and cost.

By offering an expansive set of models to choose from, along with configurable reasoning modes, developers gain a dynamic set of levers for sophisticated optimization. It all helps them calibrate the cost and performance of an entire multi-agent system to meet specific business and technical needs.

Once you select a model that fits your cost-latency-quality needs, you may have the option to fine-tune it. This specializes its knowledge and style for your specific business needs, and is done using a curated dataset of your own high-quality examples. To find out if a model permits and supports fine-tuning, review its documentation and license agreement.

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Pro tip

Use a centralized platform to discover, customize, and deploy foundation models. Look for one that offers a highly curated selection of the world’s best models, enables you to deploy with a single click, and offers enterprise-grade security from the start.

Grounding

An agent’s credibility and usefulness depend on its ability to provide accurate, trustworthy answers based on verifiable facts. This is where grounding comes in. It transforms agents into true workflow automators that are deeply and accurately grounded in your business data.

When it comes to grounding, there are three layers to consider.

1. RAG: A foundational first step

An agent’s credibility is tied to its ability to provide answers based on verifiable facts. Retrieval-augmented generation (RAG) connects an agent to a source of verifiable, real-time data—ensuring the agent acts on truth, not hallucination.

This simple retrieve-then-generate process can be applied to text, images, and other types of data. It enables lightning-fast searches of massive datasets, leading to responsive, timely decisions.

Yet, while RAG helps answer questions, it falls short on complex queries that require a deeper understanding of the relationships between data points.

2. GraphRAG: Smarter grounding

GraphRAG enriches grounding by understanding the explicit relationships between data points in a knowledge graph, and retrieving contextual data that better reflects its interconnections with other data sources. So, instead of just matching similar phrases, your agent understands how concepts relate.

Importantly, knowledge graphs give you direct control over your business logic. While standard RAG relies on model-generated patterns, a knowledge graph allows you to define and manage the specific relationships between entities, ensuring the agent respects your organization’s unique taxonomy and rules. For maximum reliability, leading organizations use a hybrid approach—combining the broad retrieval of standard RAG with the precision and control of GraphRAG.

Use case

A structured view of data from disparate sources

Financial services firms use knowledge graphs to give analysts a unified view of analyst reports, earnings calls, risk assessments, and more. This rich, interconnected web of data helps analysts discover previously hidden insights, such as intricate supply chain dependencies, board memberships that overlap across competitors, and exposure to complex geopolitical risks.

3. Agentic RAG: Dynamic reasoning and retrieval

The most powerful approach to grounding is Agentic RAG, where the agent is no longer a passive recipient of information but an active, reasoning participant in the retrieval process itself. With Agentic RAG, an agent can analyze a complex query, formulate a multi-step plan, and execute multiple tool calls in sequence to find the best possible information. This is not a replacement for traditional search; rather, it layers advanced reasoning on top of your existing RAG and knowledge graph infrastructure to resolve multi-hop queries.

This ability to perceive and reason across different data types transforms the agent from a data processor into a problem-solving tool that understands and interacts with the world in a more complete way. By empowering the agent to be an active, reasoning participant, developers can build systems capable of executing the complex, multi-step queries and long-horizon tasks that define next-generation agentic capabilities.

Pro tip

Use the retrieve and re-rank approach

Address the trade-off between recall (finding all relevant documents) and precision (ensuring retrieved documents are relevant) using the “retrieve and re-rank” approach, which widens the recall aperture to retrieve a larger-than-needed set of documents. This larger set is passed to the LLM or a specialized re-ranking service, which identifies the most relevant documents and discards any that are irrelevant or semantically opposite.

Note

Fine-tuning is not grounding. Fine-tuning adapts a model’s style and refines its knowledge on a specific task. Grounding connects the model to real-time, verifiable data sources to ensure its responses are factually accurate.

Tools

Tools are defined capabilities that enable an agent to do more than the native functions of its core reasoning model. From performing a simple internal calculation to interacting with external systems using API calls, tools bridge the gap between the agent’s reasoning and its ability to act. Because grounding is the primary way an agent retrieves new information, it is technically the most foundational tool in an agent’s toolkit.

Tools can include:

Internal functions and services: Proprietary logic or specialized code written by your own team to solve business-specific problems.
External APIs: Secure connections to third-party services that allow an agent to execute tasks in the real world.
Data retrieval and grounding: The ability to dynamically query databases (including natural language to SQL), search vector stores, or access enterprise knowledge bases. Whether it’s a simple search or a complex database query, these tools ensure the agent’s actions are based on verifiable data.
Agent collaboration: In more sophisticated systems, one agent can collaborate with another specialized agent to solve a problem. While an agent can be used as a “tool” for a specific task, the most powerful enterprise systems treat them as collaborators that securely coordinate actions across different domains.

Data architecture

Agents use different types of memory for different tasks. A robust enterprise data architecture must address three distinct needs: persistent storage for long-term knowledge retrieval, low-latency access for short-term conversational context, and a durable ledger for transactional auditing.

1. Long-term knowledge base (grounding and memory)

Long-term memory is the foundation for an agent’s intelligence, grounding, and personalization—and is distinct from the fast, short-term context of a live conversation. Its architecture has three core components:

A structured knowledge base for fact-based retrieval-augmented generation (RAG)
A persistent store for distilled user memory. Rather than storing every historical interaction, the agent generates and stores salient facts about the user, team, or task to enable a continuous, personalized experience
An operational data lake for raw material like conversation transcripts and workflow states, enabling more complex cognitive processes and future analytics

Use case

Gaining access to all relevant information

A legal agent instantly retrieves case law, internal policy documents, and training manuals to generate a legally compliant first draft of a contract.

2. Working memory (conversational context and short-term state)

This layer manages the transient information (the LLM context window) required for an ongoing task or conversation. To maintain a responsive user experience, it must provide extremely low-latency access for the iterative sequence of actions and observations being made.

Use case

Having a helpful conversation

A customer support agent maintains the state of a multi-step troubleshooting flow, remembering the user’s previously provided serial numbers or diagnosis steps to prevent repetition.

3. Transactional memory (state management and action auditing)

This layer is responsible for recording actions and state changes with strong consistency and integrity. It serves as the durable system of record—which is essential from a security standpoint and for delivering a non-repudiable audit trail for every agent-driven action.

Use case

Maintaining a durable ledger

A supply chain agent records the successful execution of a complex, multi-party purchase order, ensuring the transaction is permanently tracked and verifiable across financial systems.

Orchestration

Orchestration is the operational core that guides an agent through a multi-step task. For any process that requires more than a single action, it determines which tools are needed, in what sequence, and how their outputs should be combined to achieve a final goal.

As the agent’s executive function, orchestration is the key to creating sophisticated systems that automate complex business processes. It allows you to tackle problems that, previously, were not technically feasible—ultimately unlocking a new class of applications and user experiences.

A common and effective orchestration pattern is ReAct (Reason + Action). This framework synergizes the reasoning and acting capabilities of large language models, and establishes a dynamic, multi-turn loop where the model generates both reasoning traces (thoughts) and task-specific actions in an interleaved manner.

With ReAct, the reasoning helps the model track and update action plans, while actions gather information from external tools to inform the reasoning process. Here’s how it works:

Reason: The agent assesses the goal and the current state, forming a hypothesis about the next best step and whether a tool is required.
Act: The agent selects and invokes the appropriate tool.
Observe: The agent receives the output from the tool. This new information is integrated into the agent’s context and feeds into the next Reason step of the cycle.

Use cases

Cross-departmental HR automation

To onboard a new employee, the agent sequentially initiates actions across multiple systems. First, it creates an employee record in the system; then it triggers an API call to the IT agent to provision hardware and network credentials; and finally, it enrolls the employee in the required regional compliance training modules.

Proactive supply chain remediation

To automatically detect and resolve shipping disruptions, an agent is orchestrated to follow key steps. First, a monitoring alert triggers a tool to query alternative suppliers. It then runs a simulation tool to calculate the cost-benefit of switching suppliers versus delayed shipping. Finally, if approved by a human-in-the-loop, it executes the action to submit a new purchase order to the logistics agent.

Runtime

To deploy a functional agent prototype into a production environment at scale, you need a robust runtime infrastructure integrated with a cohesive system of services for grounding, tools, memory, sessions and the rest. This ensures your agents can operate within a secure, high-performance ecosystem capable of handling the complex demands of global enterprise growth.

A production-grade runtime environment requires:

Scalability: The infrastructure must automatically scale to handle variable loads, from zero to millions of requests. This includes both request-based load balancing and resource-based autoscaling to manage computational demands efficiently.

Security and control: The platform must provide a secure execution environment, managing identity of users and agents, org policies, tool and agent registries, network access controls, and secure communication channels (such as, TLS) to protect the agent and the data it accesses.

Reliability and observability: The system must include mechanisms for error handling and continuous monitoring. For complex debugging, the runtime must capture high-fidelity execution traces—a step-by-step recording of the agent’s reasoning and tool calls. This exposes the entire trajectory of a decision, allowing your teams to definitively answer “Why?” if an unexpected failure occurs. For high level oversight the system must include metrics for task completion, user feedback. Automation with simulations and evaluations allow confidence prior to and after deploying to production.

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