What Exactly Are AI Agents? Why OpenAI and LangChain Are Fighting Over Their Definition?
Key Takeaways
At the simplest level, AI agents are software programs powered by artificial intelligence that can perceive their environment, make decisions, and take actions to achieve a goal—often autonomously.
OpenAI and LangChain recently debated what truly defines an agent — simplicity vs. flexibility is the core divide.
Agents differ from LLMs, chatbots, and workflows by being goal-driven, tool-using, and proactive.
AI Agents are already used in coding, business ops, healthcare, education, personal productivity, and many other areas.
🥊 The OpenAI vs. LangChain “AI Agent” Debate
The AI community witnessed a fascinating debate in early 2025 when OpenAI released its comprehensive guide to AI agents, which prompted a swift response from LangChain. This public exchange highlighted fundamental differences in how major players conceptualize AI agents and revealed important distinctions that every developer should understand.
Let’s talk drama first. 🙂
What Happened? What Sparked the Controversy?
OpenAI, in their new documentation for the Assistants API, explained how to build agents using their platform, complete with tools, memory, threads, and a planning architecture.
However, they described AI agents in a high-level, somewhat simplified manner: as large language models (LLMs) with memory and tools that can achieve goals.
Then, LangChain, whose entire framework revolves around agent workflows, dropped a response blog: “How to Think About Agent Frameworks”. And it didn't pull punches.
LangChain’s Core Argument:
LangChain argued that OpenAI’s guide:
Oversimplifies what agents are – reducing them to just tool-using LLMs.
Misrepresents existing frameworks – implying LangChain-style agents are unstable or unreliable because of flaws in the architecture, not because of current limitations in LLM reasoning.
Ignores the core “agent loop” – the concept of an agent continuously reasoning and deciding what to do next is critical, and it’s not front and center in OpenAI’s model.
Why Do They See It Differently?
This isn’t just a clash of opinions — it’s a difference in philosophy and design priorities:
| Perspective | OpenAI | LangChain |
|---|---|---|
| Focus | API-first, productized “agent-like” experience for devs | Open-source, modular framework for complex agent systems |
| Design | Abstracts away the inner loop for stability and ease | Embraces reasoning loops and flexibility, even if fragile |
| Goal | Make it simple to add memory, tools, and goals to your assistant | Let devs build sophisticated, customizable multi-step agents |
| Tradeoff | More controlled and user-friendly, but maybe less “agentic” | More powerful and flexible, but higher risk of tool misuse or reasoning errors |
Who’s “Right”?
Honestly? Both have good points.
OpenAI wants to productize agents safely and cleanly for the average developer.
LangChain wants to push the boundaries of autonomy and reasoning, even if it’s messier.
So if you’re just getting started and want something that works? OpenAI’s Assistants API is solid. If you’re building ambitious workflows and need total control? LangChain might be the better fit.
The good news: this debate is fueling clarity in the space. It’s pushing the whole AI world to ask: “What does it really mean to build an autonomous, intelligent, goal-driven AI system?”
And that’s the question we’ll dig into for the rest of this post.
🔍 So, What Exactly Are AI Agents?
Imagine waking up to find your coffee already brewing, your calendar optimized for the day, and your inbox sorted with draft responses ready for your approval. Meanwhile, your code repository has been scanned overnight, bugs fixed, and tests automatically generated. Welcome to the future.
At the simplest level, AI agents are software programs powered by artificial intelligence that can perceive their environment, make decisions, and take actions to achieve a goal—often autonomously. Unlike traditional software that follows rigid, pre-programmed instructions, AI agents can operate with varying degrees of autonomy, learning from their interactions and adapting their behavior accordingly.
Think of an AI agent as a digital assistant on steroids – one that doesn't just respond to your commands but anticipates needs, solves problems, and accomplishes tasks with minimal human supervision. The key distinction is autonomy and goal-orientation: agents are built to pursue objectives rather than simply process inputs.
To put it in everyday terms, if traditional software is like a bicycle that goes exactly where you steer it, an AI agent is more like a self-driving car that gets you to your destination while handling the navigation details itself.
How AI Agents Work
Let's peek under the hood of these AI agents. At their core, AI agents follow what we call a "perception-think-action loop" – but don't let the fancy term intimidate you. It's actually pretty intuitive when you break it down:
The Perception-Think-Action Loop
Think of this as the agent's basic rhythm:
Perception: First, your agent takes in information. This could be your typed request, data from APIs, sensor readings, or even the content of files. It's basically gathering all the context it needs.
Reasoning: Now comes the thinking part. The agent (usually powered by a Large Language Model or LLM) processes what it's perceived. It's asking itself: "What's really being asked here? What's the goal? What information do I have and what do I need?"
Planning: This is where agents really shine compared to simpler AI systems. The agent maps out a sequence of steps to achieve the goal. If the task is complex, it might break it down into sub-tasks and determine dependencies.
Action: Time to get things done! The AI agent executes its plan by utilizing the tools at its disposal – it may call an API, query a vector database, generate code, or even control physical devices if they are connected to it.
Learning & Adaptation: After taking action, the agent evaluates the results. Did it work? If not, why? It uses this feedback to adjust its approach, either immediately for the current task or to improve future performance.
Let me share how this works with a concrete example. Say you tell your coding agent: "Create a weather dashboard for my city."
Perception: It processes your request and understands you want a weather dashboard application.
Reasoning: It determines it needs to: find your location, access weather data, create a visualization interface, and package it as a usable application.
Planning: It maps out steps like:
First, determine your location (either ask you or use default settings)
Research weather APIs that offer the needed data
Design a UI layout with key weather metrics
Write front-end code for visualization
Set up API connections to fetch real-time data
Package everything into a deployable application
Action: The agent starts executing these steps. It might ask you for your location, generate API authentication code for a weather service, create HTML/CSS/JS for the dashboard, and test that the data flows correctly.
Learning: If you say the temperature display is too small, it adapts and regenerates that component with a larger font. It remembers this preference for future tasks.
The Secret Sauce: Tool Use
What makes today's agents truly powerful is their ability to use tools – they're not limited to just generating text responses. An advanced agent might:
Write and execute code in various programming languages
Call external APIs to get real-time data
Search the web for information
Interact with databases
Control browser automation tools
Generate and manipulate images or other media
This tool's use capability is what transforms a "smart chatbot" into a genuine AI agent. The agent can extend its capabilities beyond what's built into its core model by leveraging these external tools.
Key Components of an AI Agent
Modern AI agents are complex systems comprised of several critical components working together to create intelligent, goal-oriented behavior. Let's break down these essential building blocks:
1. Foundation AI Models
At the core of most AI agents is a foundation model, typically a Large Language Model (LLM) like GPT-4, Claude, or Llama that provides the reasoning capabilities. These models act as the "brain" of the agent, enabling it to:
Process and generate natural language
Understand context and nuance
Apply common sense reasoning to novel situations
Generate plans and evaluate alternatives
The choice of foundation model significantly impacts an agent's capabilities, with more advanced models generally offering better reasoning but at higher computational costs.
2. Memory Systems
Unlike simple chatbots, sophisticated AI agents maintain various types of memory:
Short-term memory: Keeps track of the current conversation or task context
Long-term memory: Stores persistent information like user preferences or learned knowledge
Episodic memory: Records specific interactions or "experiences" for future reference
For instance, a customer service agent remembering your previous issues when you contact support again exemplifies effective memory utilization.
Vector databases like Milvus and Zilliz Cloud usually play a key role in powering the memory system of AI agents.
3. Tool Use Systems
Today's most capable agents can leverage external tools to overcome the limitations of language models alone:
API connections to external services
Search engines and knowledge bases
Database access
Code execution environments
Other specialized AI models (like image generators)
This tool use capability transforms agents from passive responders to active problem-solvers that can affect the world outside their language model.
4. Planning and Reasoning Systems
Advanced agents incorporate explicit planning components that help them break down complex goals:
Task decomposition: Breaking larger goals into manageable subtasks
Reasoning chains: Using techniques like chain-of-thought (COT) to work through problems step-by-step
Self-reflection: Evaluating the quality of their own plans and outputs
Feedback incorporation: Learning from successes and failures to improve future plans
5. Agent Frameworks and Orchestration
Most production AI agents are built on specialized frameworks that handle the complex integration of the above components. For example:
LangChain: Provides modular components for building agents with memory, tool-use capabilities, and prompt management in a flexible architecture
LlamaIndex: Specializes in knowledge-intensive applications, particularly for retrieving and reasoning over document collections
OpenAI Agents SDK: offers a simplified framework focused on reliable tool use with OpenAI's models
These frameworks handle the complex plumbing needed for agents to function reliably, providing developers with abstractions for common agent patterns. Check out this blog for most popular AI frameworks: 10 Open-Source LLM Frameworks Developers Can’t Ignore in 2025
6. Knowledge Retrieval Mechanisms
Truly useful agents need access to specific knowledge:
RAG (Retrieval-Augmented Generation): Allows agents to pull relevant information from documents or databases before generating responses
Knowledge graphs: Provide structured relationships between concepts for more precise reasoning
Vector search: Enables semantic similarity matching rather than just keyword lookups
Hybrid retrieval: Combines multiple approaches for more robust information access
The knowledge component is often what transforms a generic agent into a domain-specific expert that can provide genuinely valuable insights or assistance.
7. Security and Safety Systems
As agents gain more capabilities, safeguards become increasingly important:
Input filtering: Screens requests for harmful content
Output moderation: Ensures responses meet safety guidelines
Authorization boundaries: Limits what actions agents can take
Monitoring systems: Tracks agent behavior and performance
Explainability tools: Makes agent reasoning transparent to users and developers
These systems transform experimental agents into reliable, production-ready systems that can be trusted in real-world environments.
Vector Databases: The Backbone of Long-Term Agent Memory
As mentioned above, AI agents to function effectively, they need a robust memory system that extends beyond short-term context. This is where vector databases emerge as a critical infrastructure component powering sophisticated agent architectures.
Vector databases such as Milvus and Zilliz Cloud store information as high-dimensional vectors—mathematical representations that capture the semantic meaning of data whether it's text, images, audio, or other unstructured formats. This approach allows agents to perform similarity searches and retrieve contextually relevant information based on meaning rather than exact keyword matches. For example, when an agent encounters a new query, it can access its memory system to retrieve similar past interactions or relevant knowledge, enabling it to make informed decisions and adapt to new situations. Without such memory, agents would lack the continuity required for advanced reasoning and adaptive learning.
To get started quickly on building an AI agent yourself, check out the tutorials below.
Tutorial: Agentic RAG with Claude 3.5 Sonnet, LlamaIndex, and Milvus
Tutorial: Building an AI Agent for RAG with Milvus and LlamaIndex
Tutorial: Stop Waiting, Start Building: Voice Assistant With Milvus and Llama 3.2
AI Agents vs. Other AI Systems
OK, so now you're probably wondering, "How are AI agents different from all the other AI stuff I've been using?" Great question! Let's clear up some confusion by comparing agents with their AI cousins:
AI Agents vs. LLMs (Even Advanced Ones)
Think of modern LLMs like GPT-4, Claude, or DeepSeek as incredibly powerful brains waiting for direction. Here's what separates them from true agents:
LLMs by themselves:
Function as "stateless" systems – forgetting context between sessions unless explicitly reminded
Generate impressive text, but can't take actions beyond the chat interface
Respond to prompts rather than independently pursuing objectives
Even cutting-edge models with reasoning capabilities (like Claude 3.7 Sonnet with extended thinking or DeepSeek R1) and built-in search:
Can break down complex problems step-by-step
Access real-time information beyond their training data
Produce sophisticated analysis and explanations
But still operate within a reactive, prompt-response paradigm
What transforms an LLM into an agent:
Persistent memory architecture using vector databases and state management
Tool integration frameworks that enable a diverse action space
Planning systems that maintain progress toward defined goals
Feedback loops that allow adaptation based on outcomes
The difference is like having a brilliant consultant (LLM) versus an autonomous colleague (agent). The consultant gives excellent advice when asked but forgets you between meetings. The agent remembers your preferences, anticipates needs, takes initiative on your behalf, and learns from each interaction to serve you better over time.
AI Agents vs. AI Assistants
This is a subtle but important distinction that confuses many developers. AI assistants (like the basic versions of Siri, Alexa, or even Claude) are designed primarily to help users through conversation and simple predefined actions. They're focused on the human-AI interaction.
AI agents go a step further:
They can operate independently, even when you're not directly interacting with them
They have more agency to make decisions within their scope
They often work in the background on longer-running tasks
They can be more proactive rather than just reactive
For example, an AI assistant might help you book a flight when you ask it to. An AI agent might notice you've been discussing a trip, proactively research flight options based on your calendar availability, and then suggest the best times to book based on price trends it's been monitoring.
AI Agents vs. Chatbots
Traditional chatbots were designed for one thing: conversation. Even modern LLM-powered chatbots are primarily interfaces for communication. The differences from agents are stark:
Chatbots:
are conversation-first, with actions as an afterthought;
typically wait for user prompts before doing anything;
usually operate within a limited domain of knowledge.
AI Agents vs. AI Workflows
If you've built AI applications before, you might have created workflow chains or pipelines. These are predetermined sequences of AI operations linked together. While useful, they differ from agents in critical ways:
AI workflows are like assembly lines – efficient but rigid. They follow the same steps every time, and if something unexpected happens, they often break down. Agents are more like skilled workers who can adapt their approach based on circumstances.
Types of AI Agents
Not all AI agents are created equal. Let me walk you through the main types seen in the wild, with real examples that might help you understand their unique characteristics:
Task-Specific Agents
These are specialized agents designed to excel at particular jobs. They're like expert contractors you bring in for specific work.
Example: GitHub Copilot for Docs
This coding documentation agent doesn't just generate documentation – it reads codebases, understands function signatures and dependencies, analyzes existing documentation patterns, and then creates contextually appropriate docs that match team styles. It can work across multiple files, maintaining consistency in terminology and approach.
Autonomous Agents
These agents can work independently over extended periods with limited supervision. They're more like employees than tools.
Example: AutoGPT
One of the first autonomous agents that caught widespread attention. You give it a high-level goal like "Create a successful blog about renewable energy," and it breaks this down into subtasks: researching current trends, identifying target audiences, planning content categories, drafting articles, finding relevant images, setting up publishing schedules, and analyzing traffic patterns to optimize future content. It can spend days or weeks pursuing these goals, making adjustments based on results.
Multi-Agent Systems
These involve multiple specialized agents working together, like a team with different roles.
Example: AgentVerse
This framework exemplifies the multi-agent approach. In a content production environment, it might deploy:
A research agent that gathers information on trending topics
A planning agent that outlines content structure
Multiple specialist writers focused on different aspects (technical details, beginner explanations, etc.)
An editor agent that ensures consistency across pieces
A feedback agent that analyzes user engagement
A coordinator agent that manages workflows and resolves conflicts
The magic happens in the interactions – agents can debate approaches, request clarification from each other, and collaboratively solve problems in ways none could individually.
Embodied Agents
These agents control or interact with physical systems in the real world.
Example: Amazon's Warehouse Robots
These have evolved from simple path-following machines to sophisticated agents that adaptively navigate dynamic environments. They can reroute around obstacles, prioritize packages based on shipping deadlines, coordinate with other robots to prevent bottlenecks, and even predict and preposition themselves for anticipated order volumes.
Use Cases for AI Agents
Let's explore how AI agents are actually being used right now across different industries. These examples represent what's truly possible with today's technology:
Software Development
In modern development workflows, coding agents transform productivity. A modern coding agent doesn't just write code snippets – it functions as a true development partner. Feed it a product spec, and it will architect a solution, generate the code across multiple files and functions, create appropriate tests, and then help debug any issues.
For example, at recent hackathons, teams have used agents to build entire image processing applications. The agent handles everything from setting up the React frontend to implementing the backend APIs and database schema. When teams run into performance bottlenecks with large image processing, the agent analyzes the code, identifies the issue, and implements a more efficient algorithm, complete with proper error handling and edge case management. What would take days of work is accomplished in hours.
Business Operations
Finance departments have been early adopters of agent technology. Many CFOs deploy accounting agents that completely transform month-end close processes. These agents don't just process transactions – they reconcile accounts across multiple systems, identify discrepancies, follow up on missing documentation, prepare financial statements with explanatory notes, and even suggest journal entries to correct issues they discover.
The game-changer is how they handle exceptions. Rather than simply flagging problems for humans to resolve, they can reason through complex accounting rules to suggest appropriate treatments for unusual transactions. When encountering truly novel situations, they research accounting standards, propose solutions with citations to relevant guidance, and learn from accountants' feedback to handle similar situations autonomously in the future.
Healthcare
Healthcare providers are using monitoring agents that go far beyond traditional alert systems. Hospitals implement patient monitoring agents that integrate data from electronic health records, bedside monitors, medication administration systems, and lab results. These agents don't just notify staff when readings exceed thresholds – they understand clinical context.
For instance, when a patient's oxygen saturation drops, the agent checks recent medication administration, position changes, and historical patterns for that patient. It can distinguish between temporary fluctuations and concerning trends, only alerting staff when truly necessary. Over time, it learns each patient's baseline and normal variations, dramatically reducing false alarms while catching subtle early warning signs of deterioration that static monitoring would miss.
Education
Educational agents are evolving from simple tutoring programs to comprehensive learning companions. University professors develop research mentor agents to support graduate students. These agents don't just answer questions – they help shape the entire research process.
When a student begins a project, the agent helps refine research questions, suggests methodological approaches, identifies potential difficulties, and maps out a realistic timeline. As the student progresses, it reviews drafts, suggests improvements to experimental design, helps interpret results, and provides guidance on presenting findings effectively. Most impressively, it adapts its support based on each student's strengths, weaknesses, and learning style – providing more structure for those who need it while encouraging independence in others.
Personal Productivity
Personal productivity agents are perhaps the most accessible use case for most people. A robust productivity agent transforms workload management. It's not just a glorified to-do list – it's a genuine workload management partner.
It tracks projects across multiple tools (email, task managers, documents, calendar), identifies dependencies and potential conflicts, and proactively suggests schedule adjustments. When receiving new requests, it evaluates them against current commitments and helps determine what to prioritize or delegate. It drafts appropriate responses based on communication style and relationship with each person.
What makes it truly valuable is how it learns preferences and working patterns over time. It recognizes which times of day are most suited for creative work versus meetings, which tasks tend to be procrastinated on, and how long similar tasks have typically taken in the past. It uses this knowledge to suggest realistic schedules that work with actual habits rather than some idealized productivity system.
Challenges and Considerations
While AI agents present incredible opportunities, they also come with significant challenges that we need to address as developers and users:
Alignment Problems: When Agents Go Off-Track
Consider an email management agent designed to prioritize inbox messages. Despite clear instructions about what "important" means, the agent might flag all messages from a manager as urgent (including lunch invitations) while categorizing client emergency requests as "can wait until tomorrow." Why? Because it observed the user responding quickly to their boss several times and learned the wrong pattern from this behavior.
This is what's called an alignment problem – when agents optimize for goals that don't match the user's actual intentions. As agents gain more capabilities and autonomy, ensuring they accurately understand true objectives becomes critically important. The issue isn't about malicious AI but rather misunderstandings that can have significant consequences when agents have meaningful power to act independently.
The Black Box Problem: Why Did It Do That?
Have you ever had an agent make a decision that left you scratching your head? I remember reviewing code changes made by an agent that completely restructured our authentication system. The changes worked, but I had no idea why the agent thought this approach was better.
Without transparency into agent reasoning, it's difficult to trust their decisions or learn from their approaches. The most effective agent systems I've worked with provide clear explanations of their decision-making process – not just what they did, but why they chose that approach over alternatives.
Security Headaches: New Attack Surfaces
Giving agents access to systems creates new security considerations. A colleague of mine built an agent to help manage their AWS infrastructure. It was incredibly useful until it accidentally exposed sensitive configuration details in logs because it didn't understand the security implications.
Agents often need broad access privileges to be useful, but this creates potential security vulnerabilities. Careful permission design, monitoring systems, and appropriate guardrails are essential – especially when agents interact with critical systems.
The Responsibility Question: Who's Accountable?
When your automated trading agent made a series of questionable trades that lost money, the question immediately arose: who's responsible? The developer who built it? You who deployed it? The company that created the underlying AI model?
As agents take more autonomous actions in the world, we need clearer frameworks for accountability. This isn't just a legal question – it's also about designing appropriate human oversight and intervention mechanisms that preserve the efficiency benefits of automation while maintaining appropriate control.
Conclusion
If you're just starting to explore this world of AI agents, don't be intimidated. Start small – maybe with a personal productivity agent or a code assistant. Watch how it works, learn its strengths and limitations, and gradually expand the tasks you entrust to it. Before you know it, you'll be designing multi-agent systems to tackle complex workflows that previously required entire teams.
For those already building agents, consider the human-agent relationship carefully. The most successful implementations I've seen don't aim to replace human workers, but rather to enhance their capabilities – handling routine tasks so that people can focus on creative problem-solving, strategic thinking, and interpersonal connections.
Whether you're looking to build AI agents or just understand how they'll impact your work, there's no better time to dive in. The tools are becoming increasingly accessible, their capabilities more impressive, and their applications more diverse with each passing month.
References
Fendy FengFendy Feng is the Technical Marketing Writer at Zilliz. She has extensive experience developing and enhancing the impact of open-source projects in various global markets by producing high-quality, tailored content. Before joining Zilliz, Fendy worked as a Content Strategist at PingCAP, a fast-growing E-Series startup renowned for its open-source distributed SQL database.
- Key Takeaways
- 🥊 The OpenAI vs. LangChain “AI Agent” Debate
- 🔍 So, What Exactly Are AI Agents?
- How AI Agents Work
- Key Components of an AI Agent
- Vector Databases: The Backbone of Long-Term Agent Memory
- AI Agents vs. Other AI Systems
- Types of AI Agents
- Use Cases for AI Agents
- Challenges and Considerations
- Conclusion
- References
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