Agentic AI: In-Depth Introduction

Introduction

What is Agentic AI? Agentic AI refers to AI systems (often powered by large language models) that can autonomously perceive, reason, act, and learn to achieve goals without continuous human guidance​. In simpler terms, an AI agent isn’t just a static chatbot answering one question at a time – it’s a proactive entity that can make decisions, take multi-step actions using tools, and improve from experience. For example, a traditional chatbot might only give information when asked, but an agentic AI assistant could proactively handle a complex task: breaking it down, gathering data, executing steps, and adjusting its plan based on outcomes​. This proactiveness – the ability to decide what needs to be done next to fulfill an objective – is what sets agentic AI apart from standard AI apps. It’s why experts call agentic AI the “next frontier” in AI, poised to enhance productivity across industries by autonomously solving multi-step problems​.

Evolution and Key Capabilities: The concept of agents in AI isn’t entirely new – earlier AI systems and multi-agent systems have existed for years – but modern Agentic AI has surged with advances in LLMs and cognitive architectures. Early 2023 saw projects like AutoGPT and BabyAGI demonstrate how an LLM-based agent could recursively plan and act towards an objective with minimal human input​. These prototypes revealed key capabilities that define agentic AI today: sophisticated reasoning, iterative planning, tool use for taking actions, and continuous learning from feedback​. In essence, an agent perceives its environment or context (via inputs or data), reasons about how to achieve its goal (often with an LLM “brain” generating a plan), acts by invoking tools or APIs, and then learns from the results to refine future behavior​. This perceive–reason–act–learn cycle allows agents to handle tasks that are too complex for a single prompt-response interaction.

To illustrate the core components of an agent, consider Figure 1, which shows a high-level architecture of an LLM-powered agent. The Agent Core (central green module) is the decision-making brain coordinating everything. It takes in user requests, accesses a memory module (to recall context or past interactions), consults a planning module (for multi-step strategy), and invokes external tools (APIs, databases, functions) to act on the world. These pieces working together enable autonomy – the agent core uses the planning module to reason about the task, memory to stay context-aware, and tools to carry out actions – all with minimal human oversight.

Benefits for Businesses: For businesses, Agentic AI represents a leap from static automation to smart, adaptive automation. Unlike a script or traditional software that only does exactly what it’s programmed to, an AI agent can handle dynamic scenarios – it makes decisions when faced with new information and can figure out novel solutions within its scope. This means companies can delegate more complex processes to AI. For instance, a customer service agentic AI might not only answer FAQs but also check a customer’s account status and carry out transactions if appropriate​.

NVIDIA’s example describes a support agent that, while chatting with a customer, autonomously looks up the user’s account balance, analyzes which payments are due, suggests a solution, and even completes a transaction when the user agrees​. All of this happens in one seamless interaction, without a human rep manually intervening at each step. The business benefit is clear: such agents can provide 24/7 service, reduce workload on staff, and handle multitasking (answering questions and performing actions) at scale.

Real-World Impact and Use Cases: Agentic AI is already transforming various domains by taking on tasks of increasing complexity. In customer support, companies are deploying AI agents to automate routine inquiries, personalize responses, and even handle transactions, leading to faster service and higher customer satisfaction​. Some businesses are experimenting with “digital employees” – AI agents that serve as virtual receptionists, IT assistants, or sales reps that can interact in natural language and carry out user requests end-to-end. In knowledge work, agents act as research assistants or content creators. For example, marketing teams use generative agents to draft personalized content (saving hours per piece) so humans can focus on strategy​. In software development, agentic AI can help generate code snippets, triage bugs, or even collaborate on simple features, effectively becoming a junior coder that offloads grunt work​. There are also specialized domains: in healthcare, an AI agent could sift through medical records and help doctors by summarizing patient histories or monitoring follow-ups​, and in operations, agents monitor IoT sensor data and trigger alerts or adjustments autonomously. The breadth of use cases – from automating email scheduling to orchestrating entire business workflows – highlights that agentic AI’s impact is limited only by creativity and careful design​. Early reports show that a majority of companies are already exploring AI agents, seeing them as a path to significant efficiency gains in the next few years​.

Why Now and What’s Next: The rise of agentic AI has been fueled by the maturity of generative AI (which gave agents a fluent brain) and improved integration frameworks (which give agents “hands and eyes” through tools and memory). In 2024, frameworks like LangChain and others made it much easier to build custom agents, sparking wide adoption. Businesses that successfully implement agentic AI stand to gain a competitive edge – imagine having a tireless workforce of AI-driven assistants handling everything from internal data analysis to customer engagement. However, it’s important to note that with great autonomy comes great responsibility: ensuring these agents align with company policies, stay within ethical boundaries, and know when to defer to humans are crucial considerations. In the following articles, we’ll delve deeper into how agents work under the hood, common design patterns, and practical guides to start building agentic AI – equipping you to leverage this powerful technology in business scenarios.

Common Workflows and Architectures

Building an effective agentic AI system requires understanding its workflows and architectures – essentially, how an agent thinks (planning), remembers (memory), uses tools, and even collaborates with other agents. In this article, we break down common agent workflows like the planning-execution loop, discuss memory and tool integration, and explore multi-agent system architectures (from single autonomous agents to teams of agents working together). We’ll also walk through an agent’s typical lifecycle and illustrate these concepts with diagrams.

The Agent Loop: Planning and Execution Cycles

At the heart of most agent workflows is a loop of planning and execution. A single iteration of this loop can be summarized as: 1) decide on an action (plan), 2) execute that action, 3) observe the result, then repeat if the task isn’t done​. This loop allows an agent to break down complex problems into a sequence of smaller steps, adjust on the fly, and eventually reach a solution. One popular design following this paradigm is the ReAct framework (Reason + Act)​. In a ReAct agent, the large language model is prompted to intersperse thoughts (internal reasoning) and actions (calling tools) repeatedly. For example, if asked a complex question, an agent’s reasoning might be: “Thought: I should search for X… Act: [calls Search tool]… Observation: got some info… Thought: now I should calculate Y… Act: [calls Calculator]…”, and so on​. This Thought → Act → Observation cycle (repeated as needed) is a core workflow that gives agents a way to iteratively approach a task rather than trying to answer in one shot.

However, the basic ReAct loop has limitations: it only plans one step at a time and requires an LLM call at every step, which can be inefficient or lead to myopic decisions​. To address this, more advanced workflows introduce an explicit planning phase. A powerful pattern is the Plan-Execute loop (sometimes called a two-stage agent): first, the agent uses an LLM to generate a multi-step plan for the task, then it executes those steps one by one, possibly re-planning if needed​. This way, the agent has a bigger-picture roadmap from the start, reducing the risk of going in circles or taking suboptimal steps. After executing each step, the agent can check if the goal is met; if not, it can refine or continue the plan.

The diagram below (Figure 2) illustrates a Plan-and-Execute agent architecture. When a user request comes in (1), the agent’s planner component creates a series of sub-tasks (2) that need to be done. These tasks go into a Task List (which might look like a to-do list: step 1, step 2, step 3, …). Then, a Single-Task Agent executor picks up each task in sequence and enters its own loop of trying to solve that task (3) by possibly using tools (depicted by the wrench/screwdriver icon). After each step, the agent updates its state or memory with results (4). The agent can also decide to re-plan if new tasks emerge or a different approach is needed (5b), or finalize and respond to the user when done (5a). Figure 2 shows how the agent might circle back to re-plan if the initial plan didn’t fully solve the problem – creating a feedback cycle until the goal is achieved.

Another variant is ReWOO (Reasoning Without Observations), which optimizes the loop by letting the planner assign names or variables to intermediate results and reuse them​. In a ReWOO agent, the plan might look like: “Plan: do X; Execute step E1; Plan: use result of E1 to do Y; Execute step E2; …”, effectively allowing the agent to carry some state between steps without re-calling the planner for every single action​. The key takeaway is that agent workflows often alternate between a thinking phase and an action phase. Simpler agents intermix small thoughts and actions (ReAct), whereas more advanced ones separate a global planning phase (make a plan, then act on it). Choosing the right loop depends on the complexity of the task and efficiency needs.

Integrating Memory into Workflows

Memory is what allows an agent to maintain context beyond a single step or query. Without memory, an agent would be short-sighted – it wouldn’t recall previous user inputs or its own past actions. There are typically two types of memory in agent architectures:

• Short-term memory (working memory): This includes the immediate history of the dialogue or recent observations. For example, an agent engaged in a conversation will keep the last few turns in context so it can refer back to what’s already been said.
• Long-term memory (knowledge base): This is stored knowledge the agent can query, such as a vector database of documents, past cases, or any data relevant to its domain.

In practice, frameworks often provide memory modules or caches that agents consult. For example, an agent might retrieve relevant facts from a company wiki (via a knowledge base tool) when a user asks a question, a technique known as retrieval augmented generation (RAG). Memory becomes crucial in multi-step tasks – imagine a research assistant agent that reads several documents: it needs to “remember” key points from each to compile a final report. In our agent loop, memory is consulted during the reasoning/planning stage (“what have I learned so far that helps with the next step?”) and updated during the learning stage (“store this new information for future queries”).

Advanced agent designs use memory to avoid repeating work. After each act/observation cycle, the agent can append a summary of what was done to its context. This running log is sometimes called the agent’s internal scratchpad. Some systems also maintain state objects separate from the LLM’s token memory – e.g. a Python dict that stores variables or outcomes – which the agent can use. The NVIDIA technical blueprint for agentic AI emphasizes a “data flywheel,” where interactions continuously feed back to improve the underlying models or knowledge base​. In business settings, this means an agent can get smarter over time (learning which solutions work best, or updating its knowledge when new data arrives).

From an architectural perspective, memory can be implemented as part of the agent core (storing conversation state in-memory) or as an external component like a vector store or database. The agent core might ask the memory module, “Recall any info relevant to topic X,” at the start of reasoning. Many frameworks like LangChain and LlamaIndex provide easy hooks for integrating memory, so that when you build an agent, you can attach a memory object (e.g. ConversationBufferMemory in LangChain) to automatically handle context persistence. We will see examples of this when discussing frameworks.

Tool Use and Integration

One of the defining features of agentic AI is the ability to use tools – these could be APIs, databases, search engines, calculators, or any external system the agent can call. Tools give agents capabilities beyond what the base LLM model knows. For example, even a powerful LLM won’t know real-time stock prices (since its training data is static), but an agent with a “StockPrice API” tool can fetch the latest price and then reason about it.

Agents typically have a toolbox: a set of tool functions each with a name, description, and an interface for input/output. During the planning/reasoning phase, the agent decides which tool (if any) is needed next and with what arguments. This decision is usually made by the LLM itself – it’s prompted with the list of available tool names and descriptions, and based on its understanding of the task, it will output something like “I should use Tool X with input Y”. The agent runtime will then execute that tool and feed the result back into the agent’s context as an observation​.

A simple example of tool use in a workflow: suppose an agent is asked, “How many days until the next holiday?” The agent might not know offhand, so it chooses a “date calculator” tool or a “web search” tool. The loop would be:

• Thought: “I need today’s date and the date of the next holiday.”
• Action: Use CalendarTool to get today’s date (observation: 2025-03-21).
• Thought: “Now search for the date of the next public holiday.”
• Action: Use HolidayAPI tool (observation: “Next holiday is 2025-04-01”).
• Thought: “Calculate difference between dates.”
• Action: Use DateDiff tool (observation: “11 days”).
• Response: “It’s 11 days until the next holiday.”

Tool integration architecture-wise means the agent core must interface with external functions. In frameworks, tools are often implemented as Python functions (for local tools) or API call wrappers, and the agent uses an agent executor that knows how to map the LLM’s output to calling these functions. Some modern LLMs also support native function calling (like OpenAI’s API where the model can return a JSON object to call a function), which frameworks utilize under the hood to make tool use more reliable.

A critical aspect of tool use is designing good tool descriptions – the agent decides based on the description, so it should clearly state what the tool does. For instance, a tool that sends emails might be described as “EmailSender – sends an email to a specified address with a subject and body. Useful for notifying users.” If descriptions are ambiguous, the agent might misuse tools or get confused (e.g. mixing up a database query tool vs a web search tool). Best practices involve giving examples in the prompt of how to use each tool.

Finally, developers often implement guardrails around tools. Since an agent could potentially perform actions that have side effects (like modifying data, posting messages, etc.), it’s common to have checks – for example, limit an agent’s access (it shouldn’t call “delete_all_records” unless explicitly allowed) or have a human approval step for certain actions. We’ll discuss human-in-the-loop and safety later, but it’s worth noting here that while tools greatly expand an agent’s capabilities, they also require careful control.

Multi-Agent Architectures: Collaborating Agents

So far we focused on a single agent. But what if you have multiple agents working together? This leads to multi-agent systems, where each agent could have specialized roles or tasks, and they coordinate to solve a bigger problem. In a business context, you might envision an “agent team” where, say, one agent handles data gathering, another does analysis, and a third composes a report – all collaborating to deliver a result.

There are several architectures for multi-agent systems, each defining how agents communicate and coordinate​:

• Network (Fully-connected): Every agent can talk to every other agent freely. This is like a group chat where agents share information and decide who handles what on the fly. It’s very flexible – any agent can call upon any other – but can get chaotic without a protocol.
• Supervisor (Hub-Spoke): One agent is designated as the leader or coordinator, and all other agents only communicate through this supervisor​. The supervisor might break a task into sub-tasks and assign them to worker agents, gather their results, then make the final decision. This is analogous to a manager delegating tasks to a team.
• Supervisor as Tools: A special case of the above where the workers are not fully autonomous in deciding when to act; instead, the supervisor agent “calls” the other agents as if they were tools. In this setup, the main agent is an LLM that decides which sub-agent (tool-agent) to invoke at each step​ Each sub-agent performs its function (perhaps itself an LLM or a program) and returns a result.
• Hierarchical (Tree): This extends the supervisor model into multiple layers. You might have a top-level agent that delegates to mid-level agents, which in turn delegate to lower-level agents. This can mirror organizational structures (e.g., department head agent -> team lead agents -> individual task agents). Hierarchies can handle complex workflows by breaking them into nested sub-problems.
• Custom (Graph): A catch-all category where you define a specific communication graph. Maybe agent A sends output to B and C, then C triggers D, etc. Parts of the flow might be hardwired (deterministic routes), and some decisions of who to call next might be dynamic​. This allows tailoring the collaboration pattern to the problem (akin to designing a pipeline with agent modules).

The diagram below (Figure 3) compares these architectures. A Single Agent (top-left) simply has an LLM connected to some tools (orange diamonds). A Network (top-middle) shows four agents all interconnected. A Supervisor topology (top-right) shows one central agent with directed arrows to three others (hub and spoke). The bottom row shows a Supervisor (as tools) design where an LLM (blue) can call three other agents (green) as if they were tools. A Hierarchical example (bottom-middle) has a top agent overseeing two sub-agents, each of which oversees their own smaller agents. Custom (bottom-right) depicts an irregular graph of agents with specific communication flows.

Top row: Single Agent (one agent using tools), Network (fully connected agents), Supervisor (one central agent directing others). Bottom row: Supervisor as Tools (one agent calls others like APIs), Hierarchical (layers of agents in a tree), and Custom (arbitrary directed graph of agent interactions).

Designing multi-agent workflows involves deciding how agents pass information. Some patterns use a shared memory or blackboard, where agents write and read interim results from a common store. Others use direct messaging or function calls. The LangGraph framework, for example, treats each agent as a node in a graph and supports passing a Command to transition control from one agent to another (a mechanism for “handoffs”)​. Handoffs can include passing along state (data payload) so the next agent has the context it needs​.

When do you need multiple agents? Often when a task can be naturally split into distinct roles or requires different expertise. For instance, an “AI project manager” agent could oversee a “coder” agent and a “tester” agent when building software. In a customer service scenario, one agent might specialize in technical queries and another in billing issues, and a supervisor agent routes customers to the right specialist. Multi-agent systems can also implement a form of debate or collaboration: two agents with different viewpoints might discuss to reach a better answer (one playing the role of a critic and one as a proposer, for example).

It’s important to note that multi-agent doesn’t always mean many separate large models – sometimes it could be the same underlying LLM prompted to behave as different personas, or a mix of an LLM agent plus non-LLM automation. But conceptually, treating them as separate agents clarifies the architecture.

Agent Lifecycle from Instantiation to Execution

Let’s walk through the lifecycle of an agent in an application:

1. Instantiation: The agent is created with a certain configuration – this includes its prompt (defining its persona or objective), available tools, memory initialization, and other parameters (like LLM model choice, temperature, etc.). For example, we might instantiate an agent with: role = “customer support agent”, tools = [KnowledgeBaseTool, CRMTool], memory = empty. In code, this is where you call something like initialize_agent(...) or construct an Agent object.
2. Receiving a Task/Query: The agent gets an input – say a user question or a new task trigger. This kicks off the agent’s reasoning. The input, along with relevant context (e.g., an initial system prompt and memory), forms the full prompt given to the LLM.
3. Planning/Reasoning: The agent (via the LLM) analyzes the query. It might break down the problem and decide on a first action. In the prompt, this is where you often see the agent’s “Thought:” followed by an intended “Action:”.
4. Action Execution: The agent’s decision is parsed by the framework. If it’s a tool invocation, the specified tool is called with the provided arguments. The environment (which could be the internet, a database, etc.) is affected or queried, and a result is obtained.
5. Observation: The result of the action (tool output or new information) is fed back into the agent. Typically, the framework will append something like “Observation: [result]” to the agent’s context.
6. Loop (Further Planning): Based on the observation, the agent’s LLM reasoning kicks in again – deciding whether the goal is achieved or another action is needed. The cycle of Thought→Action→Observation repeats until a stopping condition.
7. Completion: A stopping condition might be that the agent decides it can answer the user or the task is done. At this point, the agent produces a final output (which could be a textual answer, or a completed transaction, etc.). The loop ends.
8. Learning/Memory Update: After completion, the agent (or the system orchestrating it) may store the interaction in memory. If a learning mechanism is in place, it could update its knowledge base or adjust some strategy. In long-running agents, this is crucial for improving over time; for ephemeral agents (single-use instances), learning might simply mean logging the outcome for developers to review later.

Throughout this lifecycle, especially in steps 3–7, proper logging and traceability is important in business apps. Many frameworks offer callback hooks or trace logs so you can see each Thought/Action/Observation – this is useful for debugging agent behavior or auditing its decisions.

Visualization of Lifecycle: You can think of it like a flowchart: Start -> (Input) -> [Agent Thinks] -> [Agent Acts] -> [Got result?] -> if not done, loop back to Agent Thinks -> … -> End with Output. If a human approval step is involved, the flow might pause at some actions waiting for a human to confirm before proceeding (common in high-stakes domains, e.g., an agent can draft an email but a person must approve sending).

In summary, agentic AI workflows are about cycling through reasoning and acting, maintaining state via memory, and possibly juggling multiple agents. By mastering these patterns – whether a simple loop or a multi-agent orchestration – developers can design AI systems that are robust, efficient, and capable of tackling complex, real-world tasks. Next, we will see how to implement these ideas using popular frameworks, which abstract a lot of these details and provide ready-made components for agents, memory, and tools.

The next article in the Agentic AI Series is a Getting Started Guide to agentic frameworks.

Until the next one,

‍

Cohorte Team

March 21, 2025