You’ve probably noticed AI agents showing up everywhere: chatbots that handle support, tools that book reservations, even systems that automate behind-the-scenes work.
They’re built to take in info, remember key details, make decisions, and follow through (without needing a human to guide every step). Large language models have only made them smarter and more useful.
At Azumo, we build AI agents with the best AI agent frameworks out there, like LangChain and LiveKit.
We also use tools like LangGraph and LangSmith, which are part of the LangChain stack. These help build more advanced, stateful agents and monitor their performance.
To appreciate their value, it’s important to understand what AI agents really are.
What Are AI Agents?
AI agents are able to reason and act without being instructed by you at each step. You give them some information, such as a question, request, or a fact. Then, they figure out what needs to happen next. These applications are the logical next step for many of the LLMs in use today.
As a result, AI agents are not like simple one-way scripts. A well-designed AI agent can understand what is going on (the context), recall important facts (has working memory), make decisions (reason), and perform the right actions to complete a task (have agency). It can also learn from new information as it goes along.
Key Features of AI Agents
The majority of AI agents are built around four central elements. You can find these in chatbots or software that handles background tasks.
1. Input
First, there is the requirement to gather information. This may be from a typed message, voice instruction, web page, or data source. The agent looks at that input and tries to figure out what it is being asked to do.
2. Memory
The agent should be able to remember what has been handled before. For instance, if a person wants to book a hotel, it makes sense for the agent to remember details like the city and check-in date instead of asking again. Or, if it's crawling a web page, it should know what parts it has already looked at. Remembering these kinds of things enables it to work more effectively and not waste time doing things twice.
3. Planning
Then, after the agent has obtained its input and context, it decides. This may be applying business rules or creating a simple plan. The aim is to figure out how to get from where it is to where it wishes to be.
4. Action
Once the plan is complete, the agent acts. That may mean sending a response, calling an API, updating a system, or invoking a process. The agent converts its decision into reality and value.
When all four parts work together, the result is a system that can think, act, and make work smarter. That is why AI agents are more effective than mere automation.
At Azumo, we use frameworks like LangChain to put these pieces together. This allows us to focus on building smart, reliable agents that talk to real business systems and deliver real value.
What Role Do AI Agent Frameworks Have in Developing AI Agents?
Now that you know what AI agents are and how they work, it makes sense to look at how we actually put them in place.
While you can start from scratch and create an agent, that usually means writing a good amount of custom code simply to get the basics working.
You need to manage memory, schedule the agent's actions, process input and output, and integrate all this with external tools. That gets messy pretty fast.
Agentic AI frameworks address that problem. They already have the basic building blocks in place, so you can focus on what makes your agent useful, not the wiring underneath.
As I mentioned earlier, at Azumo, we use frameworks like LangChain and LiveKit. In a recent project, using LangChain cut the development time in half. It let us focus on writing the decision logic of the agent, instead of wiring everything together by hand.
But be warned, frameworks are not perfect. They can be time-consuming to learn and may limit how much you can customize them. However, in most projects, especially where speed and scale are issues, they are the smart move.
To make it easier to see where frameworks actually help, we’ve grouped the main benefits into four areas. These are based on what we run into most often when building agents for real projects.
To show you how AI Agent frameworks help, we've classified the main benefits into four simple categories.
Build Faster Without Starting from Scratch
AI agent frameworks already have pre-fabricated pieces for handling inputs, holding context, and controlling simple workflows. This is quicker and simpler.
Keep Conversations Moving
Real AI agents need to remember what was said before. Without memory, they repeat questions or lose track of what they are doing.
Connect to the Tools You Already Use
Most AI agents have to pull or push data from other systems. AI agent frameworks make it simple to integrate with CRMs, APIs, databases, or even hardware.
Launch Faster and Scale with Less Effort
Launching is easier when the AI agent framework supports cloud deployment and background processing. You don't have to rebuild the system when you scale.
Key Features of AI Agent Frameworks
When we build AI agents at Azumo, we do not want to spend our time reinventing the basics. A good AI agent framework gives us a good foundation, and then we can focus on what makes the agent really useful. Over time, we've found a few things that really make a difference in choosing the right tool for the job.
The Fundamentals Should Already Be There
We don't need to waste time coding up fundamentals that every agent needs. That includes handling inputs, memory management, deciding on the next action, and sending a response. A good AI agent framework gives us these pieces pre-made so we can focus our time on the special pieces of the project, not rewriting the same setup repeatedly.
It Should Be Able to Handle Real-World Agent Logic
AI agents must be able to do more than answer one question. They must have multi-step flows, ask for missing information, and sometimes hand off work to other systems. If the framework helps us build that kind of logic nicely, everything works fine. Otherwise, we end up implementing workarounds just to get it to work.
Communication Should Be Simple to Understand and Manage
We’ve seen agents break because data gets passed around in messy or inconsistent ways. AI agent frameworks that give us a clear structure for how agents send and receive messages are much easier to work with. They make the agent’s behavior more predictable and help us avoid hard-to-find bugs.
Connecting to Other Tools Should Be Simple
Agents almost always need to integrate with something outside the application. That could be a database, a CRM, a messaging platform, or another thing. When the AI agent framework supports those kinds of integrations or at least makes them easy to do, we can get things done faster and don't need to write a whole bunch of custom code that is hard to support.
We Need to Know What the Agent Is Doing
When things go wrong, we'd like to understand why. The systems that offer us visibility on how the agent is working help us debug and have confidence in it. Logging, testing, and being able to observe what the agent is doing allow us to build and fix things faster.
In my experience, these features are the difference between a tidy build and a long, frustrating project. The best frameworks take care of the plumbing so we can focus on building agents that actually solve problems.
Best AI Agent Frameworks That We Use for Our Projects
We've referenced LangChain and LiveKit a few times in this post, and we're not kidding. They are the frameworks we see in real-world use most often, and the toolsets, that our dev teams use on a daily basis.
Here's a quick summary of what they do, where they shine, and when you might want to use them.
LangChain
LangChain is one of the most popular open-source tools for building AI agents from large language models. It's absolutely free and gives our development team all the components we need to create agents that can reason, plan, remember, and chat with tools or APIs. We've used LangChain extensively in many projects, especially when we need fine-grained control over what an agent does at several steps.
You can just add memory modules, using external tools, long-term context, or multi-step workflows. It is straightforward to use: be it building a simple chatbot or a more sophisticated system that pulls data from external sources.
Key Features
- Modular structure for chaining prompts, tools, memory, and logic
- Straightforward integration with APIs, databases, and cloud services
- Functions well with retrieval-augmented generation (RAG) setups
- Supports both synchronous and streaming outputs
- Active open-source ecosystem with regular updates
Use Cases
LangChain has enabled us to build customer service agents, document summarizers, and internal tools for a wide range of businesses, including several Fortune 500 companies. It's particularly great when the agent has to carry context, employ multiple tools, or make decisions on structured data.
Pricing
LangChain itself is free and open source. If you need more tools to debug, monitor, or test your agent, you can use LangSmith. It's meant to be used with LangChain and helps you see what your agent is doing in the background.
- Developer (Free): 1 seat, 5,000 traces/month, debugging, testing, and monitoring features.
- Plus ($39/user/month): Adds up to 10 seats, higher rate limits, and email support.
- Enterprise (Custom): For businesses that need SSO, SLAs, or private deployment options.
- Startups (Discounted): Personalized pricing with high trace allowance for early-stage companies.
LiveKit
LiveKit is an open-source real-time audio/video framework built on WebRTC. It's a foundational tool for building voice-first and audio-reactive AI agents. We've used LiveKit in projects that required low-latency live audio streaming.
Key Features
- Live audio and video streaming with minimal latency
- SDKs for web, mobile, and server-side apps
- Scalable infrastructure that can handle thousands of concurrent users
- Secure connections and native support for live media pipelines
Use Cases
LiveKit is ideal when your agent needs to talk. We’ve looked at it for outbound call agents, voice-driven assistants, and anything that needs to stream human-AI conversations in real-time. It also works well for telehealth, education platforms, and virtual events.
Pricing
- Build (Free): Includes 100 concurrent users, 5,000 connection minutes, and 50GB of bandwidth.
- Ship ($50/month): Adds up to 1,000 concurrent users, 150,000 minutes, and 250GB bandwidth.
- Scale ($500/month): For production-level use, with 1.5 million minutes and 3TB bandwidth included.
Use Cases & Industry Applications
Let’s be real—at this point, it’s hard to think of a business that wouldn’t benefit from using AI agents in some way. They’re quickly becoming a must-have, not just a nice-to-have.
Be it serving customers, doing repetitive tasks, or untangling messy data, there is often a clear use case for doing things faster, more efficiently, or simply to make them more manageable.
Some agents interact directly with people, like in customer service or sales. Others work quietly in the background, automating tasks that would normally take hours to complete. In both cases, we’ve seen companies get real results from using agents where it counts.
Here are some instances where we've seen it firsthand:
Customer Support and Engagement
Many of the AI agents we’ve built are designed to help enterprises connect with their customers in more natural and meaningful ways—whether through voice or chat. One example is the Alexa Skill we built for Discovery Channel, called Quizcovery, which offered a fun and interactive way for users to engage with the brand through trivia.
Another example is Charlibot, our AI-powered chatbot platform built to deliver personalized, real-time conversations. You can train Charlibot in minutes using your existing content—like PDFs or your website—and it works across popular platforms such as Slack, SMS, WhatsApp, and Facebook Messenger.
Internal Automation and Operational Efficiency
Not all agents speak to customers. Some work entirely in the background. One of our energy customers had a large issue with spurious pipeline sensor alarms. When the system said there was a potential leak, they were sending a crew way out into the desert, only to find that there was nothing wrong. We created an agent that learned how to tell whether an alert would likely be real. That by itself saved them a lot of time and money.
We’ve done similar things in finance and logistics, too. Agents help clean up unstructured input, make quick decisions based on patterns, and trigger the right action without needing a human to step in.
Sales and Marketing Automation
We’ve also developed support agents that answer common questions, collect basic info, and pass it along to the right person if needed. Lately, we’ve seen a lot of interest in voice-based agents for call centers. For example, think of a setup where an AI makes the first part of a cold call. If the person’s interested, it hands things over to a real salesperson, using their voice so the switch feels smooth and no one notices the change.
This kind of arrangement minimizes burnout and keeps your team concentrated on real opportunities, not dead ends.
Future Trends and Innovations in AI Agent Frameworks
We’ve seen AI agents change fast over the last couple of years. What used to be simple scripts answering basic questions are now turning into full systems that handle live input, adapt as they go, and plug into real tools. In one project, we helped build an audio agent that could handle cold calls.
It didn’t just speak to people. It held a short conversation, figured out if the person was interested, and passed the call to a real salesperson while keeping the tone and context consistent. The only reason that worked was because the framework supported real-time voice input and context tracking.
Multimodal input is also starting to matter more. People don’t just send messages. They talk, upload files, share images, and jump between channels. Older systems couldn’t keep up with that.
Now, we’re seeing frameworks that let us build agents that handle all of it in one workflow. We’ve used that to make interactions smoother and less frustrating, especially when people come in with half-formed questions or scattered information.
One of the biggest shifts we’ve noticed is how agents manage memory. In the past, every message was treated like a new conversation. That didn’t work for things like insurance applications, healthcare forms, or multi-step support issues.
Large language models are driving a lot of this. They’re not just there to make the agent sound smart anymore. They’re doing the reasoning. They’re figuring out what’s missing, asking for follow-ups, and even choosing which tools to call or what action to take.
We’ve used frameworks like LangChain to wire all this together without needing to build every piece by hand. That’s made a huge difference in how quickly we can go from idea to working product.
The business side is shifting too. Most companies we talk to have already used AI in some part of their workflow. Statista’s report shows that around 72 percent of businesses are using it in at least one function. That tells us people aren’t just trying to save money. They’re trying to rethink how they work.
We’ve seen it firsthand. These tools are becoming part of how teams actually run their business, not just something on the side.
And this is just the beginning. The frameworks are getting stronger, and what we can build with them keeps expanding.
AI Agent Frameworks
If you’ve made it this far, you’ve probably got a good feel for what AI agents can do, what frameworks help build them, and how they’re showing up across industries.
With the proper framework, AI agents can do everything, like directing support tickets, backing up real-time voice interactions, or executing complex tasks in the background.
We've seen for ourselves how frameworks like LangChain and LiveKit speed up development and make it easier to build, scale, and maintain agents.
However, the right choice of framework will depend on what you're building, how much control you need, and with what systems the agent needs to integrate.
At Azumo, we help companies make those choices and bring agent-based solutions to life. If you're working to build something with real impact, we want to hear what you're creating.