What Are AI Agents?

An AI agent is not necessarily a single model.

A functional agentic system can combine several components into a continuous operational loop.

A simplified architecture looks like:

1. Objective→
2. Context→
3. Reasoning→
4. Planning→
5. Tool Use→
6. Action→
7. Observation→
8. Next Decision

Each component plays a different role.

1. Objective

Every agent needs something to work toward.

An objective might be:

• resolve a customer request;
• investigate an operational issue;
• prepare a report;
• monitor a system;
• identify potential risks;
• coordinate a workflow;
• collect and analyse information.

The objective provides direction.

Without it, the system may generate useful information but has no clear definition of what it is trying to accomplish.

2. Context

Before acting, the agent needs relevant information.

Context may come from:

• enterprise databases;
• documents;
• knowledge systems;
• APIs;
• previous interactions;
• real-time data;
• other AI systems.

For example, an enterprise agent asked to investigate a delayed order may need access to:

• order information;
• inventory systems;
• logistics data;
• customer history;
• operational policies.

The quality of an agent’s decisions depends significantly on whether it has the right context at the right time.

3. Reasoning and Planning

The agent evaluates the objective and available information to determine possible next steps.

A complex objective may be broken into smaller tasks.

For example:

Investigate why production output has decreased.

The agent might determine that it needs to:

• retrieve recent production data;
• compare it with historical performance;
• identify significant changes;
• check machine or maintenance records;
• analyse possible contributing factors;
• prepare findings.

This process is often described as planning or agentic reasoning.

The specific implementation varies. Not every system needs the same reasoning architecture, and production systems should avoid assuming that an agent’s generated plan is automatically correct.

4. Tool Use: Where AI Agents Become Operational

One of the defining capabilities of an AI agent is the ability to interact with external tools and systems.

Depending on permissions, an agent may use:

• search and retrieval systems;
• databases;
• APIs;
• CRMs;
• ERP platforms;
• email systems;
• calendars;
• workflow engines;
• code execution environments;
• enterprise software.

For example, instead of answering:

“You should create a support ticket.”

An agent could potentially:

1. retrieve the relevant information→
2. create the ticket→
3. assign it to the appropriate team→
4. update the user

This is a fundamental transition.

The AI is no longer only describing what should happen.

It can become part of the system that makes it happen.

5. Action

After reasoning and selecting an appropriate tool, the agent can execute an action within its defined permissions.

Actions may include:

• retrieving information;
• updating a record;
• generating a document;
• creating a task;
• sending a notification;
• triggering a workflow;
• assigning work;
• interacting with another agent.

The level of action should be determined by risk.

For example:

Low-risk action

An agent formats a report.

Medium-risk action

An agent creates a support ticket.

Higher-risk action

An agent modifies a financial, operational or security-related system.

As the potential impact increases, governance and human approval can become increasingly important.

6. Observation and Feedback

After an action is performed, the agent can evaluate the result.

• Did the tool return the expected information?
• Was the task completed?
• Did the action create a new problem?
• Is another step required?

This creates a feedback loop:

1. Think→
2. Act→
3. Observe→
4. Adapt

An agent may repeat this cycle multiple times while working toward its objective.

The terms are often confused.

A chatbot and an AI agent may both use similar language models, but their system design and operational role can be very different.

The distinction is not always absolute.

A chatbot can be connected to tools.

An AI agent can have a conversational interface.

The more important question is:

Generative AI focuses on creating outputs.

These outputs can include:

• text;
• images;
• code;
• audio;
• video;
• structured content.

AI agents may use generative AI as one of their underlying capabilities.

However, an agent adds additional layers.

A generative model may answer:

“Here is a strategy for reducing customer churn.”

An AI agent could potentially:

• analyse customer data;
• identify high-risk accounts;
• retrieve interaction history;
• generate recommended actions;
• create follow-up tasks;
• notify the appropriate team;
• track the outcome.

In this sense:

An agentic workflow is a process in which AI agents participate in completing one or more stages of a task.

For example:

Objective: Analyse a market opportunity.

An agentic workflow could involve:

1. Research Agent

   Collects relevant information

2. Analysis Agent

   Identifies patterns, opportunities and risks

3. Strategy Agent

   Develops possible recommendations

4. Review Layer

   Checks the output against defined criteria

5. Human Decision-Maker

   Approves, modifies or rejects high-impact recommendations

This creates a workflow where intelligence is distributed across specialised functions.

The architecture begins to resemble a digital organisation of specialised capabilities.

Not every problem requires multiple agents.

A simple task may be handled by a single well-designed agent.

However, more complex workflows can potentially involve multiple specialised agents.

Single-Agent System

One agent handles a complete workflow. Example: retrieve sales data, analyse performance and prepare a report.

Multi-Agent System

Multiple agents perform specialised functions. Example: a Data Agent retrieves information, an Analysis Agent identifies patterns, a Research Agent gathers external context, an Operations Agent coordinates actions and a Review Agent checks outputs.

The value of a multi-agent architecture is not simply having more agents.

More components can also introduce more complexity, latency and coordination challenges.

The right architecture should be based on the problem being solved.

A production-grade AI agent can include several layers.

Intelligence Layer

The AI model or models responsible for interpreting information and generating reasoning or outputs.

Memory and State

Mechanisms for maintaining relevant information during or across tasks — short-term task state, conversation context, retrieved knowledge and approved long-term information.

Tool Layer

The interfaces that allow an agent to interact with external systems.

Planning Layer

The mechanisms used to break an objective into steps and determine the next action.

Orchestration Layer

The system responsible for coordinating agents, models, tools and workflows.

Governance Layer

The rules defining permissions, access, boundaries, approvals, monitoring and escalation.

Memory should not simply mean storing everything indefinitely. Systems need clear rules around what information is retained and why.

Together, these components transform an AI model into a potentially operational intelligence system.

The most significant impact of AI agents may emerge inside organisations.

Enterprise environments contain large numbers of repetitive but information-intensive workflows.

Consider a simple request:

“Find out why this customer order is delayed.”

A human employee may need to:

• open a CRM;
• check an order management system;
• review inventory;
• contact logistics;
• examine recent communications;
• prepare a response.

An AI agent could potentially coordinate parts of this process.

It might:

• retrieve the order;
• identify its current status;
• check inventory;
• retrieve logistics information;
• analyse relevant events;
• prepare a summary;
• draft a response;
• request human approval when necessary.

The goal is not necessarily to replace every person involved.

It is to reduce the distance between:

1. Question→
2. Information→
3. Decision→
4. Action

AI agents can become the agency layer of an autonomous intelligence system.

Consider a connected architecture.

1. Perception

   Vision AI and sensors observe the environment

2. Local Intelligence

   Edge AI processes information near the source

3. Context

   Enterprise systems and cloud infrastructure provide additional information

4. Reasoning

   AI models interpret the situation

5. Agency

   AI agents determine and coordinate next steps

6. Action

   Workflows, software systems, machines or people execute the response

This creates a larger intelligence loop:

1. Perceive→
2. Understand→
3. Reason→
4. Coordinate→
5. Act→
6. Learn

An individual AI agent may be one component.

A network of connected technologies can create the broader autonomous system.

AI agents can potentially support a wide range of functions.

Research and Knowledge Work

Retrieve information, analyse documents, synthesise findings and prepare structured outputs.

Customer Operations

Investigate requests, retrieve account information, prepare responses and trigger approved workflows.

Business Operations

Help coordinate reporting, task management, document workflows and operational analysis.

Software and Engineering

Assist with code analysis, testing, debugging, documentation and development workflows.

Physical Operations

When connected to Vision AI, Edge AI, sensors and operational systems, participate in workflows that connect digital intelligence to physical environments.

AI agents should not be treated as universally reliable autonomous employees.

They have limitations.

Incorrect Reasoning

An agent can misunderstand a task or reach an incorrect conclusion.

Tool Errors

An external system may return incomplete or incorrect information.

Context Gaps

The agent may not have access to all relevant information.

Permission Risks

Giving an agent access to powerful systems without appropriate controls can create operational risk.

Unpredictable Behaviour

Complex workflows can produce unexpected outcomes, particularly when agents interact with multiple tools or other agents.

For this reason, agentic capability must be matched with governance.

A useful model for enterprise AI is not complete autonomy.

It is adaptive autonomy.

The level of human involvement can depend on the situation.

This creates a more realistic approach to autonomous intelligence.

As AI agents gain access to tools and systems, governance becomes critical.

Every enterprise should consider questions such as:

• What information can the agent access?
• Which tools can it use?
• What actions can it execute?
• What actions require human approval?
• How are actions recorded?
• How can an action be reversed?
• What happens when the agent encounters uncertainty?
• Who is accountable for the system?

A capable AI agent without clear boundaries can become an operational risk.

A well-designed AI agent should understand not only what it can do, but also:

For SVARA, AI agents represent an important layer in the evolution from AI tools to connected intelligence systems.

A Vision AI system

can see.

An Edge AI system

can process locally.

Generative and Cognitive AI

can interpret and reason.

Digital Twins

can simulate and model.

AI Agents

can coordinate tasks and actions.

An AI OS

can help orchestrate the entire intelligence environment.

The architecture becomes:

1. Data→
2. Perception→
3. Intelligence→
4. Agency→
5. Action

The agent is not the entire system.

It is part of the bridge between intelligence and execution.

The next generation of AI systems is likely to become increasingly:

• specialised;
• connected;
• multimodal;
• tool-enabled;
• context-aware;
• governed;
• collaborative.

Rather than relying on one AI system to perform every task, organisations may increasingly deploy networks of specialised intelligence.

• One agent may analyse.
• Another may retrieve.
• Another may simulate.
• Another may coordinate.
• Another may review.

Humans remain responsible for defining objectives, establishing boundaries and governing high-impact decisions.

The long-term opportunity is not to create an AI that replaces every process.

It is to redesign how intelligence participates in those processes.

The evolution may look like this:

1. Software executes instructions.
2. Automation follows workflows.
3. Generative AI creates outputs.
4. AI Agents pursue objectives.
5. Autonomous intelligence connects perception, reasoning, agency and action.

The most important development in AI may not be the ability to generate increasingly convincing answers.

It may be the ability to connect intelligence to work.

AI agents represent a transition from “What can AI tell me?” to “What can AI help me accomplish?”

But capability alone is not enough.

The future of agentic AI depends on building systems that are not only intelligent—but also connected, observable, governed and aligned with human objectives.

The future is not simply AI that can think.

It is intelligence that can understand, coordinate and act—responsibly.