Agent AI – what is it, how does it work and what is it for? …

Agent AI is a goal-oriented system. It not only responds in conversation, but can also independently carry out tasks in the digital world using tools (e.g. API). In practice, that means less manual work. An agent can plan actions, fetch data from systems and only stop once it has met the success criterion (e.g. created a ticket and returned its ID). Security is equally important. An agent’s autonomy operates only within the permissions and policies granted, and some actions may require human approval. In this article, we explain how an AI agent differs from a chatbot, what its features are, and what layers a typical deployment architecture is made up of. You will also learn how memory, tools and verification mechanisms affect the quality and predictability of its operation. Read on if you want to understand when an AI agent makes sense in company processes and how to approach the topic in practice.

Definition and key features of an AI agent

An AI agent is a system that, in addition to conversation, can carry out a task in a digital environment, for example place an order, create a ticket or generate a report. The most important difference compared with a chatbot is that an agent has a planning mechanism, context memory and access to tools (e.g. API), rather than just a “text conversation”. In practice, an agent is designed around a goal rather than a single response, so it breaks the problem down into smaller steps and carries them out one by one (e.g. “fetch data → filter → send e-mail → update CRM”). This approach makes it easier to see the task through to completion, instead of stopping at recommendations alone.

An agent can operate autonomously, but only within the boundaries of the permissions and policies set, and the level of autonomy can be scaled. A common pattern is moving from “suggest” mode (the agent proposes steps) to “execute” mode (the agent performs operations), with additional limits for risky actions. This means an agent can, for example, create invoices, but may not have the right to send them without approval. This approach makes it possible to combine automation with control.

Memory in an agent means storing the information needed to continue work, such as a case ID, customer preferences or the results of earlier tool calls. This may be short-term memory (conversation context) and long-term memory (e.g. a vector database with notes about the customer), which answers questions such as: “will the agent remember that I prefer PDF invoices?”. It is also important that good implementations do not rely solely on the “knowledge of the model”. An agent combines the language model with rules and data verification in source systems (e.g. API, SQL), and in mature scenarios can return cited sources, for example a link to a document in Confluence.

A key feature of an agent is working with tools: it can run searches in a database, execute scripts, create calendar entries or send messages. For example, an HR agent can retrieve the number of holiday days from Workday and prepare a request if the user confirms it. An agent can also be multimodal, meaning it is not limited to text, but also works with images and files, for example analysing an invoice PDF or performing OCR on a scan. When it has to operate in an application interface, it may use RPA automation (e.g. UiPath) or browser tools, which further increases the importance of control and auditing.

1. 01Goal and planningBreaks the problem down into smaller steps and carries them out one by one.
2. 02Tools and actionUses API, carries out tasks in a digital environment.
3. 03Autonomy within boundariesActs independently, in line with the permissions granted.

An AI agent focuses on achieving a goal through active action and context memory, not just conversation like a chatbot.

AI agent architecture: what is it made of?

The architecture of an AI agent includes the model (LLM) and layers that support planning, tool execution, memory and security controls. At the centre is the model that creates the plan, decisions and messages (e.g. GPT-4.1, Claude, Llama 3), and costs usually rise with the number of tokens and steps. In practice, a multi-step agent is often 3–10× more expensive than a single chat response, because it more often “thinks, acts and checks” over several iterations. For this reason, in the architecture, the mechanisms for controlling the workflow and limiting unnecessary calls are just as important as the model itself.

• Base model (LLM) – generates the plan, decisions and communication content.
• Orchestrator and control loop – manages the “think–act–check” cycle, keeps track of the number of iterations and stop conditions.
• Tools and connectors – a set of functions the agent can invoke (REST/GraphQL, SQL, search engine, calendar, CRM, ticketing system).
• Operational memory and knowledge layer – connects conversation context with long-term memory (e.g. Postgres + pgvector, Pinecone, Weaviate, Milvus) and enables RAG.
• Policies and guardrails – access rules, PII handling and enforcing approval for specific actions (e.g. using NeMo Guardrails or GuardrailsAI).
• Observability and auditing – logging of steps, tool results and metrics (e.g. LangSmith, OpenTelemetry, Arize Phoenix).
• UI integration layer – running the agent where users work (Slack, MS Teams, a web app or an API endpoint).

The orchestrator ensures that the agent does not get stuck in endless searching and does not launch a tool without the required authorisation. The tools and connectors layer translates “intent” into specific actions in systems: for example, a Jira connector can allow the agent to create an issue with “priority” and “component” fields and assign it to a team based on the incident description. Memory and RAG make it easier to answer questions about company policies based on documents, rather than relying solely on the model’s general knowledge. As a result, an agent can, for example, first search for a document and only then formulate an answer based on it.

The layer of policies, guardrails and audit is fundamentally important, because it determines what the agent can do, what data it can access and in which situations it should ask for approval. In practice, this covers data access rules, handling of PII and blocking risky commands, as well as an audit trail of actions and their outcomes. Observability lets you answer the question “why did the agent make that decision?”, because it records actions, tool results and metrics such as response time, the number of tool calls or error rates. Integration with the UI (e.g. Slack or MS Teams), on the other hand, addresses a common implementation issue: employees do not want to learn a new tool, so the agent is brought into their everyday working channel.

How an AI agent works: the typical workflow

An AI agent works in a repetitive cycle, in which it first clarifies the goal and constraints, and only then plans and executes actions in tools. At the outset, it establishes intent: “should I only prepare a draft, or also send the email?”, and asks for missing information (e.g. dates or the client ID) to reduce the risk of mistakes at later stages. The more precisely the task boundaries and required input data are defined, the fewer corrections and escalations appear during execution. This approach is particularly important when the agent is to carry out operations in company systems.

When the task is more complex, the agent creates a plan and breaks the problem down into sub-tasks, e.g. “check invoice status → calculate the balance → prepare a message → log a note in the CRM”. In many implementations, the outline of the plan is shown in brief (1–5 steps), while fuller details are placed in audit logs. The agent then selects tools appropriate to the goal and its permissions, e.g. SQL for a sales report and an API for updating customer data. Well-designed implementations enforce parameter validation so that the agent does not perform an action without key information (e.g. the amount, currency and recipient confirmation).

After each step, the agent evaluates the result and, if necessary, adjusts its approach, e.g. when a tool returns a 401/403 error or search fails to find a document. It closes the task only when it meets a measurable success criterion, such as “ticket with ID X created” or “file saved in folder Z”, and it can return statuses and IDs from source systems. When a critical error appears (lack of permissions, contradictory data), the agent should escalate: ask a human for a decision or pass the case to an operator, e.g. by creating an incident in ServiceNow with a full parameter log. In many systems, feedback of “helped/didn’t help” is also collected, which closes the improvement loop and feeds updates to prompts, rules and, in some cases, fine-tuning or equipping the system with additional knowledge sources.

1. 01Clarifying the goalAnalysis of intent, constraints
2. 02Context analysisAsking for data
3. 03Planning sub-tasksBreaking down a complex problem
4. 04Executing actionsOperations in tools

Key principle: Precisely defining task boundaries prevents corrections and escalations during execution in systems.

Approaches to building agents: patterns and strategies

Agents are most often built on proven patterns that organise planning, tool use and quality control. ReAct (reasoning + acting) is based on alternating reasoning with actions performed in tools, instead of trying to “make up” everything without ongoing verification. The “plan and execute” approach separates the planning stage from the execution of step by step, module by module, which makes supervision and auditing easier, e.g. in an onboarding process with clearly defined success conditions at each stage. In more demanding tasks, multi-agent setups with roles (e.g. “analyst”, “verifier”, “executor”) are also used, which can be slower, but usually improve quality in areas such as due diligence or contract analysis.

When the agent is to answer based on company documents, the foundation is RAG (Retrieval-Augmented Generation), that is, first retrieving sources and only then generating the response. In practice, this makes it possible to cite specific policy clauses (e.g. “data retention”), rather than producing general interpretations. In critical processes, the best solution is often a combination of a deterministic workflow (e.g. BPMN, n8n, Temporal) with an agent that supports the “soft” stages, such as classification, summarisation or data extraction. This setup addresses the dilemma of “should the agent control everything?”: key steps (e.g. posting) are usually more sensibly based on deterministic logic.

In integrations, function calling and schemas are also important, that is, returning data in a structured form (e.g. JSON), which reduces the risk of mistakes when passing parameters to tools. Another mechanism that improves quality is self-checking (“critic”), in which a second module evaluates the result, catches contradictions, missing sources or policy violations. This directly answers the question “does the agent check itself?”, but it usually increases cost through additional model calls (typically by +20–60% tokens). The choice of pattern depends on whether the priority is speed, auditability or minimising errors in multi-step tasks.

Tools and frameworks for building AI agents

To build AI agents, frameworks are used that combine a language model with tools, memory and a controlled action loop. LangChain makes it easier to connect an LLM with functions and connectors, while LangGraph lets you model states and loops (e.g. “ask → search → verify → escalate”), which is useful where an agent has to return to search when the results are poor. LlamaIndex focuses on the data layer, i.e. document indexing, chunking and retrieval, which is why it is often chosen when the goal is to quickly launch RAG on documents (e.g. PDF and Confluence). If an agent is to answer based on company sources, choosing indexing and retrieval tools is just as important as the model itself.

In production applications, APIs are often used that simplify the management of conversation threads, files and tools, e.g. OpenAI Assistants API and Responses, where an agent can operate per client and execute functions such as “create_ticket()”. In the Microsoft ecosystem, a popular option is Microsoft Semantic Kernel, which supports building agents and “skills” in .NET and integrations with Azure, making it easier to plug the agent into Microsoft 365 environments (e.g. Teams and SharePoint) with access control. In multi-agent scenarios, AutoGen and CrewAI are used, allowing work to be divided into roles (e.g. “researcher”, “writer”, “reviewer”) and results to be passed between agents.

Long-term memory and RAG are usually implemented using vector databases and a search layer, e.g. Pinecone, Weaviate, Milvus or Postgres with pgvector, and OpenSearch/Elasticsearch for full-text search. The quality and stability of the deployment are supported by evaluation tools such as Ragas (for RAG), DeepEval or custom “golden sets”, which make it possible to compare metrics after a prompt change. To observe behaviour and metrics in practice, tools such as LangSmith and Arize Phoenix are used, so that the cost per task and the repeatability of results can be assessed.

1. 01Connecting APIs and MemoryConnecting the LLM with tools and data.
2. 02Modelling States and LoopsHandling returns (e.g. LangGraph).
3. 03Data Indexing (RAG)Quick access to PDF and documents (e.g. LlamaIndex).
4. 04Production SimplificationUsing APIs to manage threads.

The key is choosing tools that integrate the model, processing logic and specific company data sources for the agent to operate effectively.

Applications of AI agents in practice

AI agents in practice AI in practice take over repetitive end-to-end tasks in business processes, from case classification to carrying out actions in systems. In customer service, an agent can classify tickets, answer FAQs based on the knowledge base and create cases in Zendesk or ServiceNow, and by taking over 20% of simple tickets (at an average time of 8 minutes) the team can realistically save hours of work each week. In sales, an agent is often used to prepare a call summary, fill in fields in Salesforce/HubSpot and suggest next steps based on the client’s history, while good implementations require email approval and limit follow-ups (e.g. max. 2 in 14 days). The greatest value comes from implementations where the agent does not only “suggest”, but completes the task in the tools, with control and approval where necessary.

• Finance and accounting – an agent can extract data from invoices (OCR), match it to orders and prepare a package for posting, usually without posting without control. Automatic detection of missing items (e.g. tax ID, order number) reduces the number of manual corrections and returns to suppliers.
• ITOps and SecOps – an agent can diagnose incidents, check logs, metrics (Prometheus) and statuses (PagerDuty), suggest remediation steps and create a postmortem. It is often given read-only access to logs, while actions such as restarts require approval and are logged.
• HR and onboarding – an agent answers questions about policies, leave and benefits and also runs onboarding (checklists, hardware requests, access), e.g. generates a list of mandatory training courses based on role and location and creates calendar events.
• Data analysis and reporting – an analytical agent can create SQL queries, prepare charts and summarise results for non-technical people. To reduce the number of mistakes, a “SQL with preview” mode is often used, in which the user sees the query and the result before the report is published.
• Marketing and content ops – an agent can generate content variants, maintain brandbook compliance and plan the publication schedule, while simultaneously verifying facts and sources. For example, it prepares 5 headline versions and selects the best one according to the agreed rules (e.g. ≤ 60 characters, keyword at the beginning).

In many areas, the common denominator is reducing risk through control of actions, e.g. approving messages before sending or narrowing permissions to read-only in sensitive systems. In practice, such implementations work well where the expected outcome can be clearly defined (e.g. creating a case, filling in fields in the CRM or preparing a package for posting) and the steps where a human is needed can be indicated. This makes it possible to use automation without shifting responsibility for critical decisions to the model itself.

Risks, limitations and security of AI agents

The risks associated with AI agents stem mainly from the fact that the system not only generates responses, but also performs actions in tools and on company data. The most common issues are hallucinations and factual errors, especially when the agent does not have access to reliable sources or cannot find them effectively. This is limited through RAG, source citation and forcing “I don’t know”-type behaviour and escalation when a document is missing from the database. It is worth assuming that the agent should verify information in source systems (e.g. via API or SQL), rather than relying solely on content generated by the model.

Operational security depends on resilience to prompt injection and on how strictly we control what the agent does with input content, such as emails or documents. Attacks can involve injecting instructions into the data the agent reads, which is why tool isolation, filtering commands from input and policies such as “tool calls only from a controlled plan” are used, as well as content scanning. The principle of least privilege is key: the agent should have minimal permissions (e.g. read-only), and high-risk actions require approval. In practice, roles, short-lived tokens and separate service accounts are used, e.g. for creating drafts instead of carrying out irreversible operations.

Data protection concerns both what the agent “sees” and what ends up in logs and memory. When an agent processes PII, you need to enforce masking and logging rules, because logs are often the starting point for a data leak. Typical safeguards include automatic PII detection (e.g. national ID number, address) and rules such as: do not store in long-term memory and do not send to external tools without anonymisation. At the same time, it is worth maintaining an audit trail showing who requested the action, what data was used, which API was called and what result was obtained.

“Production” limitations usually come down to cost, latency and behaviour drift after changes to the model or prompt. Multi-step agents can introduce delays of 5–30 s, because they make several model and tool calls, so they are improved, among other things, by limiting iterations (e.g. max 6 steps), caching search results and using smaller models in simpler stages. After a change to the model or instructions, the agent may start escalating more often or citing sources less frequently, so regression tests on a set of scenarios and versioning of prompts and policies are needed. Legal compliance and auditability do not have one universal answer, because they depend on the data, jurisdiction and provider, but in practice they require an audit trail, retention and access control.

How to deploy an AI agent: process and best practices

It is worth starting an AI agent deployment by choosing a process with measurable value, rather than building an “agent for everything”. It is best to identify an area with KPIs such as “ticket resolution time” or “proposal preparation time”, where the result of the action can be easily assessed. Repeatability and data access also matter: if around 70% of cases look similar, the agent has a better chance of performing reliably. Such a starting point also makes it easier to define the task success criteria and security constraints more precisely.

The effectiveness of an agent depends largely on the quality of the tools and APIs it can access, which is why integration design is often as important as model selection. The API should be unambiguous, idempotent and return clear errors so that the agent can validate parameters correctly and react sensibly to problems. A proven pattern assumes separate endpoints for specific actions, e.g. “createInvoiceDraft” with field validation and a returned draft ID, rather than a generic “/doStuff”. This makes it easier to control permissions and implement approval for high-risk actions.

If the agent is to respond to policies and procedures, deployment requires preparing knowledge and RAG based on organised sources. This means versioning documents, removing duplicates and designating a “single source of truth” so that the agent does not rely on conflicting materials. In practice, chunking in the range of 300–800 tokens is used, and then recall is checked, because fragments that are too small lose context, while fragments that are too large reduce relevance. The key is that the agent can find the right document and base its answer on the source, rather than “filling in” missing facts.

Safe deployment usually relies on human-in-the-loop mechanisms, especially for critical actions. The most common setup is one in which the agent prepares a draft, a human approves it, and only then does sending or saving take place. Reversible operations and rollback logic are also designed, e.g. the ability to cancel an order within a 10-minute window, to limit the effects of mistakes. It is worth tying such rules to access policies and auditing, so it is clear who approved the action and on what basis.

Before going live, you need end-to-end tests and post-launch monitoring so that the agent behaves predictably in real-world scenarios. Usually a golden set (e.g. 200–500 cases) is built with expected outputs, and not only the response content is checked, but also the correctness of tool calls, parameters and results. After deployment, metrics such as task success rate, escalations, cost per task, response time and the causes of tool errors are tracked, and a practical warning sign is a rise in escalations, e.g. from 8% to 20% week on week. Iterative improvement usually comes down to refining prompts, tool constraints and clarification questions, rather than simply reaching for a “bigger model”, e.g. through the rule “always confirm the amount and recipient before sending”.