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1.Agents

What are agents?

Agents are goal directed intelligent actors with degrees of autonomy that can perceive information, make decisions, and take actions to achieve a goal.

Unlike traditional software that follows a fixed sequence of instructions, agents operate in degrees of autonomous loops: they observe the current state of a system, decide what to do next, and execute actions that move them closer to an objective.

In modern AI systems, an agent is typically powered by a large language model (LLM) and augmented with skills, tools, memory, and external data sources. This allows the agent to reason about a task, break it down into steps, interact with APIs or databases, and adapt its behavior based on new information.

At a high level, most agents follow a simple cycle:

  1. Observe – Gather context from user input, system state, or external data.
  2. Reason – Decide what action to take based on goals and available information.
  3. Act – Execute actions such as calling an API, querying a database, or generating a response.
  4. Update – Incorporate results and continue until the task is complete.

For example, a customer support agent might read a user’s request, search internal documentation, retrieve relevant information, and generate a response. A data agent might query multiple databases, analyze the results, and produce a report.

The key difference between agents and traditional automation is autonomy. Instead of executing a predefined workflow, agents can dynamically determine the next step based on the context of the task.

As systems become more complex, multiple agents can work together—each responsible for a specific capability such as research, planning, execution, or verification. Platforms like AgentGrid make it possible to coordinate these agents reliably, manage their interactions, and integrate them with real-world systems.

Types of AI Agents

Not all AI agents operate in the same way. Depending on how they make decisions and interact with their environment, agents can be designed with different levels of reasoning, autonomy, and planning capability. Understanding these categories helps clarify how modern AI systems evolve from simple reactive programs to more sophisticated goal-driven systems.

1. Reactive Agents

Reactive agents operate purely on the current state of the environment. They do not maintain memory of past interactions or plan ahead. Instead, they follow predefined rules that map observations directly to actions.

For example, a spam filter that flags messages based on keyword patterns behaves like a reactive agent. It observes incoming messages and immediately applies rules to determine the outcome.

Reactive agents are simple and fast, but they are limited because they cannot reason about history or long-term goals.

2. Model-Based Agents

Model-based agents maintain an internal representation of the environment. This allows them to track context over time and make more informed decisions.

Instead of reacting only to the current input, these agents consider how the system state has evolved. For example, a customer support agent may remember previous messages in a conversation and use that context to generate more relevant responses.

By maintaining a model of the environment, these agents can handle more complex tasks that require continuity and contextual understanding.

3. Goal-Based Agents

Goal-based agents go a step further by making decisions based on explicit objectives. Rather than simply responding to inputs, they evaluate possible actions according to how well those actions help achieve a goal.

For instance, a travel planning agent might receive a goal like “plan the cheapest route from Delhi to Bangalore.” The agent can then explore different flight combinations, compare prices, and select the option that best satisfies the goal.

This approach introduces reasoning and planning, allowing agents to explore multiple paths before acting.

4. Utility-Based Agents

Utility-based agents make decisions using a utility function, which measures how desirable different outcomes are. Instead of simply achieving a goal, these agents try to maximize the overall quality of the outcome.

For example, a logistics optimization agent may evaluate routes based on several factors—delivery time, fuel cost, traffic conditions, and reliability. It then chooses the route that maximizes overall utility rather than optimizing for a single variable.

This type of agent is particularly useful in systems where multiple trade-offs must be considered.

5. Learning Agents

Learning agents improve their performance over time by adapting based on feedback or experience. They continuously refine their decision-making process as they observe outcomes from previous actions.

A recommendation system is a good example. It learns from user behavior—clicks, purchases, and engagement—to refine future recommendations.

Learning agents are powerful because they can evolve in changing environments, making them well suited for dynamic real-world applications.

In modern AI systems, these categories often overlap. Many practical agents combine reasoning, memory, and learning mechanisms to handle complex tasks. As a result, modern AI agents are typically designed as hybrid systems that integrate planning, tool usage, and adaptive learning to operate effectively in real-world environments.

How Agents Work

At a high level, an AI agent operates through a continuous loop of observing, reasoning, and acting. Instead of executing a fixed sequence of instructions, the agent evaluates the current situation, decides what to do next, and takes actions that move it closer to completing a task.

This loop allows agents to handle tasks that require context, decision-making, and interaction with external systems.

1. Observation: Gathering Context

The first step in an agent’s workflow is to collect information about the current state of the task. This information can come from several sources, including user input, previous conversation history, databases, APIs, or system logs.

For example, a data analysis agent might receive a question such as “What caused the drop in revenue last quarter?” To begin working on the task, the agent may gather financial reports, recent metrics, and historical data from internal systems.

The goal of the observation step is to build enough context for the agent to understand what problem it needs to solve.

2. Reasoning: Deciding What to Do Next

Once the agent has context, it decides how to approach the problem. This stage typically involves breaking a task into smaller steps, selecting tools to use, and determining the order in which actions should be executed.

Large language models often power this reasoning layer, allowing the agent to analyze the task and plan its next move. For example, the agent might decide to:

  • Query a database for missing information
  • Call an external API
  • Perform calculations or analysis
  • Generate a response for the user

This reasoning step is what gives agents their flexibility. Instead of following a rigid workflow, the agent can dynamically decide the next action based on the situation.

3. Action: Executing Tasks

After deciding what to do, the agent performs the chosen action. Actions can involve interacting with other systems, retrieving data, or generating outputs.

Common agent actions include:

  • Calling APIs or external services
  • Running database queries
  • Executing code or scripts
  • Searching knowledge bases
  • Producing natural language responses

For example, a support agent might retrieve troubleshooting steps from documentation and generate a personalized response for a user.

4. Updating State and Continuing the Loop

Once an action is completed, the agent receives the result and updates its internal context. The output of one action often becomes the input for the next step.

For instance, if the agent queries a database and retrieves results, it may analyze those results and decide to perform additional queries or generate a report.

This cycle continues until the agent determines that the task has been completed.

The Agent Loop in Practice

In real-world systems, this loop runs repeatedly until the goal is achieved. A research agent might gather sources, analyze them, extract insights, and compile a summary. A scheduling agent might check calendars, propose meeting times, and confirm availability.

The key idea is that agents adapt their behavior as new information becomes available. Each iteration of the loop moves the system closer to completing the task.

Platforms like AgentGrid provide the infrastructure needed to manage this loop reliably—handling tool execution, context management, and coordination between different components in an agent-driven system.