What are AI Agents

𝗪𝗵𝗮𝘁 𝗮re Artificial Intelligence 𝗔𝗴𝗲𝗻𝘁s

Let's learn the basics and Beyond Artificial Intelligence 𝗔𝗴𝗲𝗻𝘁s are AI systems that are designed to reason through complex problems, create actionable plans, and execute these plans using a set of tools. AI Agents follow this 4-step continuous cycle: Number One: They Think - The agent processes available data and context. Number Two: They Plan - It decides on a strategy to achieve a goal or answer a question. Number Three: They Act - It executes the chosen plan (e.g., makes an API call, retrieves data, or interfaces with a user).

And;

Number Four: They Reflect - It evaluates the outcome of its actions, checks for errors or new insights, and uses those reflections to inform the next iteration of thought.

These four steps form a feedback loop that enables AI agents to adapt their approach as new information arrives or is acquired by them.

Does this sound familiar? Well it should. Because humans pretty much do the same when we want to perform tasks. We Think, Plan, Act and Reflect. So AI Agents are a bit like humans in that respect – not exactly but fairly close. Humans do a lot more – especially we do many things autonomously – that is on our own initiative, without someone telling us to do them.

But at the pace of developments in AI, there is a high probability that soon AI Agents will have that ability as well. So stay turned because the real show has yet to begin.

Large Language Models (or LLM’s) play the most important role in Development and Deployment of AI Agents.

Under the hood, all AI agents leverage the brains of one or more LLMs. The LLMs can be: - Large Commercially available or Open-Source publicly available models. - Small Optimized or compressed versions of LLMs that can run on local hardware more efficiently. or,

LLMs that are trained & tuned. Thanks to modern tooling, LLMs can now be developed, fine-tuned, and deployed in various environments—from the public cloud to on-premises servers. It is the LLMs the provide the core reasoning, comprehension, and language generation abilities that AI agents require to interact with us as well as machines. So the more advanced these capabilities are, the more powerful the AI Agent will be.

Currently AI Agents represent a cutting-edge frontier in Artificial Intelligence, but it’s important to note that, so far, they are firmly in the experimental and rapid prototyping phase, especially for complex, multi-step tasks.

So, while the core concepts discussed earlier are reasonably established; (that is Think-Plan-Act and Reflect loops, and the use of AI Tools), the practical deployment of AI Agents face significant hurdles. Here are the top 5 challenges:

1. Reliability & Hallucination: LLMs, the core "brains," can still generate incorrect information (which is referred to as hallucination in AI Language) or they can make flawed logical leaps. In a sequential agent loop, early errors can cascade, derailing the entire process. Thus ensuring consistent, reliable outcomes is the biggest challenge.

2. Complexity & Cost: Running sophisticated agents with multiple LLM API calls, tool integrations, and iterative loops requires significant computational resources (and of course cost). Optimizing efficiency while maintaining capability is an active area of R&D and it is expected that both the complexity and cost challenges will reduce over time.

3. Predictability & Control: Understanding exactly why an agent made a specific decision or took a specific action within a complex loop can be difficult. Debugging failures and ensuring agents operate within strict boundaries (safety, ethics, compliance) is not an easy task.

4. Tool Integration & Robustness: While agents can use tools, seamlessly integrating diverse APIs, handling authentication, parsing varied outputs, and recovering gracefully from tool failures (e.g., API downtime, unexpected response formats) requires careful engineering.

5. Evaluation: Measuring agent performance beyond simple task completion (e.g., efficiency of plan, quality of reflection, resource usage) lacks standardized benchmarks. It's harder than evaluating static RAG systems.

And,

6. Security: Agents with API access create attack surfaces for data breaches or prompt injections (one simple example is tricking a customer service agent into leaking data).

While pioneers like Boston Dynamics (robotics) and DeepMind (AlphaFold agents) demonstrate potential, overcoming these challenges requires interdisciplinary collaboration—blending AI research, systems engineering, ethics, and policy. As of 2025, agentic AI is still nascent; most deployments are narrow and supervised. The next frontier involves creating verifiably safe, self-correcting agents capable of ethical autonomy—a goal likely requiring years of iterative innovation.

Many organizations are challenged with the decision to deploy AI Agents especially when they have invested in RAG Systems already. So let’s go over Retrieval-Augmented Generation (or RAG) systems and why AI Agents is a much different paradigm altogether.

Some experts think that the adoption of AI Agents in businesses is going to be slow because of the RAG alternatives. Businesses are heavily investing in Retrieval-Augmented Generation systems because RAG provides a significant boost in factual accuracy and grounding by pulling information from specific knowledge sources before generation. This makes RAG more predictable, understandable, and lower-risk solution for many enterprise needs (for example; customer support chatbots, internal knowledge search etc.) compared to fully autonomous AI agents. RAG acts as a crucial stepping stone.

While that may be true, AI Agents can take the RAG concept to much higher performance levels. This is because AI Agents excel in workflow orchestration and control and can supervise end-to-end RAG workflows.

So AI Agents and RAG are synergistic technologies, not alternatives. AI Agents are not competing with RAG but they're actually complementary technologies. AI Agents and RAG solve different problems. RAG handles knowledge retrieval, and is good for Q&A, while AI agents manage workflows. The real synergy comes when agents supervise RAG systems. Like a project manager (agent) directing researchers (RAG).

Moreover, we should not underestimate how these two technologies layer together. Especially when you emphasize the supervision aspect - agents can monitor RAG quality, reroute queries, handle follow-ups. That's where the magic happens. One concrete example is with customer service bots where the AI Agent decides when to invoke RAG as they can manage complete RAG workflows such as:

→ Triggering RAG: Decide when a query needs retrieval (that is to "Fetch latest sales data").

→ Refining Queries: that is to Optimize input for better retrieval.

→ Validating Outputs: Check RAG responses for accuracy/hallucinations.

And

→ Multi-Tool Orchestration: which includes combining RAG with APIs, calculators, etc.

One good example is when a customer service AI agent uses RAG to pull product documents and, then invokes a warranty API. In this example, RAG is a useful tool for the AI Agent.

So RAG systems become knowledge tools in an AI agent’s toolkit for Searching the company’s knowledge base.

To wrap this up, it is not a question of "either/or" because it's not competition - mature systems will combine both. Instead of alternatives or competitors, they're actually teammates.

If you want to learn more about Retrieval-Augmented Generation (or RAG) systems, check out the link below associated with this Video.

Let’s now look at 𝗛𝗼𝘄 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 work and accomplish their 𝗧𝗮𝘀𝗸𝘀;

The Typical workflow of an AI Agent system might look like this: Step 1. The User or a system triggers a request that sets the goal for the Agent. Step 2. The Language-Model based agent uses an internal reasoning loop (which is Think → Plan → Act → and Reflect) to interpret the request and plan the next steps. Step 3. The AI agent makes calls to the requisite Generative AI APIs or enterprise data and services (via GraphQL, OpenAPI, etc.) to gather more information. Step 4. The agent processes the gathered data and formulates a response or an action. Step 5. It then executes that action or generates a final output to the user or system. Throughout all the steps above, Logging and monitoring systems record the metrics (which are typically accuracy, compliance, fairness, relevance etc.) so that developers and administrators can refine the agent’s behavior. It is important to note that AI Agents excel at autonomously executing multi-step processes that involve reasoning, information gathering, decision-making, and action. And this is one of the major reasons why sooner or later, AI Agents will replace many human workers.

Here are concrete examples with Use Cases that illustrate the Think-Plan-Act-Reflect loop;

For the first example, we’ll build an AI Advanced Personal Assistant:

o The Goal here is, which we’ll pass on to the Agent in form of a Prompt: "Plan a 5-day trip to Paris for next month, considering my budget of $2000, love for art and history, and gluten-free diet. Book necessary reservations."

o The Agent receives the input and starts Thinking: What is it Thinking? It Understands user preferences, constraints (like budget, diet, dates etc.), and the complex nature of the task (in this case it is the itinerary and al the bookings).

o The Agent then, plans on how to accomplish this task: It breaks down the task into sub-tasks: such as; Research flights, research hotels in suitable districts, draft daily itinerary with museums and historic sites, find Gluten Free restaurants near activities, check booking availability and anything else required.

o And finally the AI Agents Acts on the Subtasks:

· It Calls, rather invokes the reservation or airline Flight API to find options within budget and dates.

· It Calls the Hotel Systems API for accommodations.

· It Searches the web and maps for museums, opening hours, Gluten Free restaurants.

· It Checks booking APIs for museum tickets and restaurants.

· And then it Integrates its findings.

o It’s not done yet; it still has to Reflect on its findings. It will Check the itinerary feasibility (like the travel times between locations), budget adherence, Gluten Free options per day. And it Adjusts the plan if any conflicts are found (for example if the museum is closed on Tuesday, it will find an alternative and so on). At this point, it will Present the options to the user or another system for confirmation before booking anything. And most importantly, it Iterates as needed.

o When it’s done with the Reflect part and is satisfied, it will produce the Output: The output will be in form of a detailed itinerary draft with the flights and hotel options, booked activities (of course upon user approval), restaurant suggestions, cost breakdown and whatever else is necessary for the trip.

Now let’s look at another example. We’ll build an Automated Research Analyst:

o The Goal given to the AI Agent is: "Analyze the current market trends and competitive landscape for electric vehicle (EV) batteries in Europe for Q1 2025. Provide a summary report with key findings and risks."

o Once it receives the prompt, it will Think: In the thinking process it Identifies required data points (that is market size, key players, technological trends, regulations, risks etc.), and it will identify relevant sources (such financial databases, news aggregators, industry reports) that it will need to accomplish the job.

o Then it Plans: In this process it Defines search queries, identifies APIs and tools that it will need (for example; Bloomberg Terminal API, web search, specialized report databases and so on), and, it will outline the report structure.

o Next, it’s time to Act: It knows it has to do a lot of stuff. The job is not trivial by any means.

· It Queries financial database APIs for company performance data. This is where it will call the Retrieval Augmentation API (remember we talked about RAG’s earlier that AI Agents can use RAG as a tool)?)

· It Searches news and press releases for announcements (such as new factories, partnerships, tech breakthroughs and any other reliable and curated sources it can find).

· It Retrieves summaries of relevant industry reports.

· It also Scrapes regulatory agency websites for policy updates.

· And then, it Processes and synthesizes all the gathered data.

o As the next step, it Reflect: It Checks data consistency across sources, identifies conflicting information, assesses source credibility, evaluates if enough data exists for each section of the report. Refines searches or seeks alternative sources if gaps found.

o And finally it will produce the Output in a professional form – a Structured report summarizing market size, key players, technological advancements, regulatory environment, and identified risks.

The next example is very interesting and fairly complex. We’ll give a task to our AI Worker (our AI Agent), to Debug and Improve Software Code. Think of this as hiring a highly experienced programmer specializing in Self-Debugging & Improving Software Code:

We’ll walk through this use case step by step; Remember the four steps loop that the AI Agent follows after it is given its goal; Think, Plan, Act and reflect?

Ok, so we give our new digital team member his job instructions or Goal; The prompt could be;

o Diagnose and fix the bugs in the Bug Report. A User has reported a bug: 'The payment processing module fails when a user tries to apply discount code XMAS25.' Diagnose and fix the bug."

o The AI Agent starts thinking and in this process it Understands the bug report context (such as module, specific trigger etc.). It Recalls relevant code structure and masters it.

o Then it starts Planning: Again, not a trivial task considering the vast and complex code base. It Retrieves the payment module code. Analyzes code flow related to discount application. Reproduces the bug (as best practice and if possible via sandbox). Next it Formulates hypotheses for the failure. It Tests the hypotheses. And Proposes the appropriate fix.

o It then Acts on the Plan it has formulated to complete this task:

· Retrieves payment module code from repository.

· Runs static code analysis tool.

· Attempts to simulate the bug in a testing environment (may involve calling test APIs).

· Reviews code logic around discount validation and application.

· Identifies potential flaw (e.g., code mishandles alphanumeric codes starting with a number?).

o When it’s done, it Reflects: Tests the hypothesis by modifying test input. Confirms if the simulated bug occurs. Evaluates if the fix addresses the root cause without breaking other functionality (may run unit tests). And Iterates on diagnosis and fix if needed.

o The Output is that it Identified the bug location in the code with an explanation of the cause, suggested code fix, and updated unit test if necessary.

As you can see, AI Agents can perform everything from simple tasks – tasks that a typical executive secretary would perform to very complex task – tasks that require years of programming experience in many different programming languages and development environments.

Here is a Summary of the Key Trends in AI Agents as a refresher:

Number 1: Hybrid Approaches: Combining RAG (for reliable knowledge access) with simpler agentic loops (for specific sub-tasks like planning or tool selection) is a pragmatic path forward.

Number 2: Specialization: Development is focusing on creating robust agents for specific, well-defined tasks (e.g., automated data analysis, customer onboarding workflows) rather than general-purpose "do anything" agents.

Number 3: "Small" & Efficient Models: There's intense interest in smaller, specialized LLMs (like Llama 3-70B, Mistral, Phi-3) that can run faster and cheaper locally or on edge devices, enabling more practical agent deployment. With new developments like ‘Model Distillation’, many more options are emerging for powerful LLMs that can power AI Agents.

Number 4: Framework Maturation: Tools like LangChain, LangGraph, LlamaIndex, and AutoGen are rapidly evolving to simplify agent building, handling workflow orchestration, memory, and tool integration complexities.

Number 5: Agent Swarms: Research explores having multiple specialized agents collaborate on a single complex problem.

In Conclusion;

AI Agents represent a paradigm shift towards autonomous, goal-driven AI systems. While currently experimental and facing significant challenges in reliability and control, rapid progress in frameworks (LangChain/LangGraph, AutoGen, CrewAI), cloud platforms (Azure, Vertex, Bedrock), and underlying LLM capabilities is accelerating development. Use cases span complex planning (travel, research), automated problem-solving (debugging, support), and specialized task execution. RAG serves as a crucial, safer stepping stone for enterprises today, but agents hold the potential to fundamentally transform how we interact with software and automate intricate workflows, moving us towards a future of human-AI collaboration on complex tasks. Developers have a rich and evolving toolkit at their disposal to start building the next generation of intelligent applications.

The business world is not yet ready to fully adopt agents; they are still in the very early experimental stage, and that's why everyone is implementing RAG like crazy, as it is a safer bet. But believe it or not, AI agents are the future of AI, and at the current pace of innovation, we will soon start collaborating with AI agents instead of humans for many different specific tasks.