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🧠 What Are AI Agents?

An AI agent is a digital assistant capable of independently executing complex tasks based on a specific goal. It’s more than just a chatbot answering questions. Modern AI agents can:

• Plan multiple steps ahead
• Call APIs, work with data, create content, or search for information
• Adapt their behavior based on context, user, or business goals
• Work asynchronously and handle multiple tasks simultaneously

In short, an AI agent functions like a virtual employee — handling tasks dynamically, like a human, but faster, cheaper, and 24/7.

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Why Are AI Agents Trending Right Now?

• Advancements in large language models (LLMs) like GPT-4, Claude, and Mistral allow agents to better understand and generate natural language.
• Automation is becoming goal-driven — instead of saying “write a script,” you can say “find the best candidates for this job.”
• Companies want to scale without increasing costs — AI agents can handle both routine and analytical tasks.
• Productivity and personalization are top priorities — AI agents enable both in real time.

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What Do AI Agents Bring to Businesses?

✅ 1. Save Time and Costs

Unlike traditional automation focused on isolated tasks, AI agents can manage entire workflows. In e-commerce, for example, they can:

• Help choose the right product
• Recommend accessories
• Add items to the cart
• Handle complaints or returns

✅ 2. Boost Conversions and Loyalty

AI agents personalize conversations, learn from interactions, and respond more precisely to customer needs.

✅ 3. Team Relief and Scalability

Instead of manually handling inquiries or data, the agent works nonstop — error-free and without the need to hire more people.

✅ 4. Smarter Decision-Making

Internal agents can assist with competitive analysis, report generation, content creation, or demand forecasting.

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AI Agents in Practice

AI Agent vs. Traditional Chatbot: What's the Difference?

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What Does This Mean for Your Business?

Companies that implement AI agents today gain an edge — not just in efficiency, but in customer experience. In a world where “fast replies” are no longer enough, AI agents bring context, intelligence, and action — exactly what the modern customer expects.

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What’s Next?

AI agents are quickly evolving from assistants to full digital colleagues. Soon, it won’t be unusual to have an “AI teammate” handling tasks, collaborating with your team, and helping your business grow.

🧠 What Is Generative AI (GenAI)?

Generative AI focuses on creating content — text, images, video, or code — by using large language models (LLMs) trained on huge datasets.

Typical use cases:

• Writing emails, articles, product descriptions
• Generating graphics and images
• Creating code or marketing copy
• Customer support via AI-powered chat

But despite its capabilities, GenAI isn't a one-size-fits-all solution.

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What Other Types of AI Exist?

✅ 1. Analytical AI

This type of AI focuses on analyzing data, identifying patterns, and making predictions. It doesn't generate content but provides insights and decisions based on logic and data.

Use cases:

• Predicting customer churn or lifetime value
• Credit risk scoring
• Fraud detection
• Customer segmentation

✅ 2. Optimization AI

Rather than analyzing or generating, this AI finds the best possible solution based on a defined goal or constraint.

Use cases:

• Logistics and transportation planning
• Dynamic pricing
• Manufacturing and workforce scheduling

✅ 3. Symbolic AI (Rule-Based Systems)

This older but still relevant form of AI uses logic-based rules and decision trees. It is explainable, auditable, and reliable — especially in regulated environments.

Use cases:

• Legal or medical expert systems
• Regulatory compliance
• Automated decision-making in banking or insurance

✅ 4. Reinforcement Learning

This AI learns by trial and error in dynamic environments. It’s used when the system needs to adapt based on feedback and outcomes.

Use cases:

• Autonomous vehicles
• Robotics
• Complex process automation

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When Should (or Shouldn’t) You Use GenAI?

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What Does This Mean for Your Business?

If you're only using GenAI, you might be missing out on significant potential. The real value lies in combining AI types.

Example:

• Use Analytical AI to segment your customers.
• Use GenAI to generate personalized emails for each segment.
• Use Optimization AI to time and target campaigns efficiently.

This multi-layered approach delivers better ROI, reliability, and strategic depth.

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Summary: GenAI ≠ All of AI

🧠 What does it mean to have your data in order?

It’s more than storing files in the cloud or keeping spreadsheets neat.

When your data is “in order,” it means that:

• It’s accessible – people across the company can access it easily and securely
• It’s high-quality – data is clean, up to date, and consistent
• It has context – you know where the data came from, how it was created, and what it represents
• It’s connected – systems talk to each other, there are no data silos
• It’s actionable – the data supports decision-making, automation, and business goals

In short: Clean data = trustworthy and usable data.

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How can you tell if your data isn’t in order?

Here are some common red flags:

These challenges are common—startups, scale-ups, and enterprises all face them at some point.

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What are the risks of messy or low-quality data?

Slower decisions

Without confidence in your data, decisions are delayed—or based on gut feeling instead of facts.

Wasted resources

Analysts spend most of their time cleaning and merging data, rather than generating value.

Poor customer experiences

Outdated or fragmented data means poor personalization, errors in communication, or missed opportunities.

Blocked AI and automation efforts

You can’t build predictive models or automation without structured, clean data.

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What does it take to “clean up your data”?

Data audit

Map out your data sources, flows, and responsibilities.

Data integration

Connect systems like CRM, ERP, e‑shop, marketing platforms into a unified view.

Implement a modern data platform

Build a central, scalable place to store and manage data (e.g., a data warehouse with BI tools).

Ensure data quality

Remove duplicates, validate formats, ensure consistency.

Define governance

Set clear responsibilities for data ownership, access, and documentation.

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What’s the business impact?

✅ A single source of truth

✅ Smarter, faster decision-making

✅ Improved collaboration between departments

✅ Stronger foundations for AI, automation, and personalization

✅ More trust in your reporting and forecasts

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Final thoughts: Data isn’t just a cost. It’s an asset.

Many companies treat data as a back-office IT issue. But in reality, data is one of your most valuable business assets—and without having it in order, you can’t grow, digitize, or deliver personalized experiences.

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The Evolution of AI Agents frameworks: From Autogen to LangGraph

In the past, severallibraries such as Autogen and Langchain Agent Executor were usedto create AI agents and the workflow of their tasks. These tools aimed tosimplify and automate processes by enabling multiple agents to work together inperforming more complex tasks. But for the past several months, we have beenworking with LangGraph and felt in love with it for the significantimprovements it offers to AI developers.

Autogen was oneof the first frameworks and provided a much needed higher level of abstraction,making it easier to set up AI agents. However, the interaction between agentsfelt often somewhat like "magic" — too opaque for developers whoneeded more granular control over how the processes were defined and executed.This lack of transparency could lead to challenges in debugging andfine-tuning.

Then came LangchainAgent Executor, which allowed developers to pass "tools" toagents, and the system would keep calling these tools until it produced a finalanswer. It even allowed agents to call other agents, and the decision on whichagent to use next was managed by AI.

However, the LangchainAgent Executor approach had its drawbacks. For instance:

• It was difficult to track the individual steps of each agent. If one     agent was responsible for searching Google and retrieving results, it     wasn’t easy to display those results to the user in real-time.
• It also posed     challenges in transferring information between agents. Imagine one agent     uses Google to find information and another is tasked with finding related     images. You might want the second agent to use a summary of the article as     input for image searches, but this kind of information handoff wasn’t     straightforward.
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State of the art AI Agents framework? LangGraph!

LangGraphaddresses many of these limitations by providing a more modular and flexibleframework for managing agents. Here’s how it differs from its predecessors:

FlexibleGlobal State Management

LangGraph allowsdevelopers to define a global state. This means that agents can eitheraccess the entire state or just a portion of it, depending on their task. Thisflexibility is critical when coordinating multiple agents, as it allows forbetter communication and resource sharing. For instance, the agent responsiblefor finding images could be given a summary of the article, which it could useto refine its keyword searches.

ModularDesign with Graph Structure

At the core of LangGraphis a graph-based structure, where nodes represent either calls to alanguage model (LLM) or the use of other tools. Each node functions as a stepin the process, taking the current state as input and outputting an updatedstate.

The edges in thegraph define the flow of information between nodes. These edges can be:

• Optional: allowing the process to branch     into different states based on logic or the decisions of the LLM.
• Required: ensuring that     after a Google search, for example, the next step will always be for a     copywriting agent to process the search results.
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Debuggingand Visualization

LangGraph also enhancesdebugging and visualization. Developers can render the graph, making it easierfor others to understand the workflow. Debugging is simplified throughintegration with tools like Langsmith, or open-source alternatives like Langfuse.These tools allow developers to monitor the execution in real-time, displayingactions such as which articles were selected, what’s currently happening, andeven statistics like token usage.

TheTrade-Off: Flexibility vs. Complexity

While LangGraph offerssubstantial improvements in flexibility and control, it does come with asteeper learning curve. The ability to define global states, manage complexagent interactions, and create sophisticated logic chains gives developerslow-level control but also requires a deeper understanding of the system.

LangGraph marks asignificant evolution in the design and management of AI agents, offering apowerful, modular solution for complex workflows. For developers who needgranular control and detailed oversight of agent operations, LangGraph presentsa promising option. However, with great flexibility comes complexity, meaningdevelopers must invest time in learning the framework to fully leverage itscapabilities. That’s what we have done, making LangGraph our tool of choice forall complex GenAI solutions that need multiple agents working together.

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Reason + Act = A Smarter AI

Originally proposed in a joint paper by Princeton University and Google, ReAct introduces a method that fuses two core capabilities of LLMs:

• Reasoning, often achieved through techniques like chain-of-thought prompting
• Acting, which involves generating and executing action plans (e.g., calling tools, retrieving data)

By combining these, LLMs are no longer just passive generators of text. They become agents capable of making decisions, interacting with environments, and refining their process as they work toward solving a task.

In practical terms, ReAct allows LLMs to structure and execute a sequence of operations — pulling in external data, making calculations, and iterating until a meaningful, actionable output is reached. It’s a game-changer in how we approach task automation.

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LangChain ReAct: From Theory to Practice

At BigHub, we’re leveraging LangChain ReAct, one of the most accessible and powerful implementations of the ReAct paradigm. LangChain wraps the reasoning and acting logic into agent executors, enabling models to think step-by-step and interact with tools dynamically.

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Let’s break it down with a fun — but technically accurate — example:

> Entering new AgentExecutor chain...
  Query: "Find Leo DiCaprio's girlfriend and calculate her age raised to 0.43 power."
  Action: Search
  Action Input: "Leo DiCaprio girlfriend"
  Observation: Camila Morrone
  Thought: "Need to find Camila Morrone's age"
  Action: Search
  Action Input: "Camila Morrone's age"
  Observation: 25 years
  Thought: "Need to calculate 25 raised to the 0.43 power"
  Action: Calculator
  Action Input: 25^0.43
  Observation: Answer: 3.991298452658078
  Thought: "I now have the final answer"
  Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.
> Finished chain.

This illustrates the model's ability to reason, act, and deliver a final answer — without human intervention between steps.

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Why It Matters for Business

While the example above is playful, the business implications are profound. Take the insurance industry:
A LangChain ReAct-powered assistant could autonomously:

• Retrieve calculation formulas from internal knowledge bases
• Prompt users for missing inputs
• Perform real-time computations
• Deliver final results instantly

No hand-coded flows. No rigid scripts. Just dynamic, responsive, and intelligent interactions.

From automating customer service workflows to enabling deep analytical queries across datasets, ReAct opens the door to use cases across industries — finance, healthcare, logistics, legal, and beyond.

LangChain: A Framework Built for the Future

LangChain is an open-source, modular framework designed to help developers harness the power of Large Language Models (LLMs). With support for multiple programming languages like Python and JavaScript, it's a flexible and accessible solution for building AI-driven apps that understand context, reason through problems, and act accordingly.

From the moment we first experimented with it, we saw how LangChain goes far beyond simple prompt-response applications. It enables agent-based systems — intelligent workflows that use reasoning, tool calling, and memory to accomplish complex tasks.

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Why We Love It: Modularity, Integration, and Customization

Three things make LangChain stand out for us at BigHub:

1. Ease of Integration – Plug it into existing systems quickly.
2. Modular Design – Use only what you need, nothing more.
3. High Customizability – Tailor it to fit specific business cases without rebuilding your stack.

LangChain’s structure allows businesses to evolve their AI capabilities without needing massive overhauls — ideal in today’s fast-moving tech environment.

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Agents, Toolkits, and Use Cases

LangChain gives you the blueprint to build agents — smart components that combine reasoning with action. These agents can:

• Summarize documents
• Search databases
• Act as co-pilots in business workflows
• Power intelligent chatbots
• Answer complex queries with real-time data

Whatever the use case, LangChain's toolkit makes it easier to go from concept to prototype to production.

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From Input to Insight: How LangChain Works

LangChain isn't just code — it’s a logical flow that mirrors human reasoning. Think of it like a dynamic flowchart, where each node represents a cognitive step: understanding the query, fetching relevant data, generating a prompt, and finally, crafting a response.

Here’s a simplified LangChain expression to illustrate this:

chain = (
    {
        "query_text": itemgetter("query_text"),
        "chat_history": itemgetter("chat_history"),
        "sources": {
            "chat_history": itemgetter("chat_history")
        }
        | RunnableLambda(lambda x: create_prompt(x["chat_history"]))
        | model_2
        | RunnableLambda(search_azure_cognitive_search),
    }
    | RunnableLambda(lambda x: create_template_from_messages(x["chat_history"]))
    | model
)

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This isn’t just syntax — it’s a story. A structured process that makes AI responses more relevant, informed, and conversational.

We've always focused on leveraging the latest technologies to deliver top-notch solutions. From the start, one of our most important partnerships has been with a tech leader in cloud and AI – Microsoft. This year has been full of exciting updates, from becoming a Microsoft Managed Partner to achieving the latest Fabric Featured Partner status.

_{What is Microsoft Fabric?}

Earlier this year, Microsoft unveiled Microsoft Fabric in public preview – an exciting end-to-end analytics solution that combines Azure Data Factory, Azure Synapse Analytics, and Power BI. This all-in-one analytics powerhouse features seven core workloads seamlessly integrated into a single architecture, enhancing data governance with Microsoft Purview, simplifying data management with OneLake, and empowering users through Microsoft 365 integration. With advanced AI features like Copilot, Fabric boosts productivity and offers partners opportunities to create innovative, value-driven solutions, making data environments more secure, compliant, and cost-effective.

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Being a Fabric Featured Partner means we’re skilled in using Fabric and ready to guide our clients through its implementation. Our team has undergone extensive training and with many clients where Fabric is already running, we can provide top-notch support and insights.

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_{What this means for our customers}

Microsoft Fabric is a comprehensive data platform designed to streamline your organization's data processes. By unifying various data operations, it significantly reduces the costs associated with operating, integrating, managing, and securing your data.

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• Stay ahead with cutting-edge tech: Partnering with BigHub, you can access advanced features and updates that will keep your solutions state-of-the-art.
• Better support: Our close relationship with Microsoft, and access to their experts and resources, enables us to resolve even the trickiest issues quickly and solve everything correctly.
• Boosted productivity with AI: Using Fabric helps both our customers and our team to enhance productivity, and enjoy significantly faster data access and time-to-delivery.

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Back in 2016, two friends, Karel Šimánek and Tomáš Hubínek, decided to take a different path. They were passionate about artificial intelligence long before it became trendy, and despite everyone advising them against it, they stuck to their vision. The result? BigHub—a company specializing in advanced data solutions born out of their determination to do things their way.

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Clients that trust us

Our unique journey and perseverance were recognized first abroad, with first clients from Germany, and the US, thanks to our cooperation with GoodData. But our domestic market quickly matured and we started to work for enterprise clients in Czechia and Slovakia. The first clients were mainly and quite surprisingly from the energy sector, ČEZ, E.ON, PPAS, and VSE. But we always saw the potential of AI technology for transforming every business. Our goal has always been to make AI accessible for all kinds of businesses, big or small.

While large enterprise clients are still our primary clientele, thanks to the enormous growth of opportunities that came with GPT, we've been opening up to small and medium-sized businesses too. Our GPT-based solutions offer clients better access to data analytics, powerful automation of back-office processes, and upgrade of their services - for a fraction of the price it would cost 8 years ago.

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Wide recognition of our expertise

We’ve had some incredible milestones along the way, which helped BigHub to be recognized as a leader in applied AI. In 2021, BigHub ranked among the top ten fastest-growing tech companies in Central Europe in the prestigious Deloitte Technology Fast 50 CE ranking. Not bad for a company that started with just a few friends and a lot of ambition!

BigHub became widely recognized in 2023 when we won the Readers' Choice Award for the best business story in the EY Entrepreneur of the Year competition. Apart from the successes in awards, we gained recognition in prestigious outlets like Forbes, Hospodářské noviny, CzechCrunch, and EURO.cz.

Both of our founders have shared their expertise on major platforms like CNN Prima NEWS, where they provided valuable tips on spotting and protecting against deepfake and how to spot them, and in a series of interviews for Roklen24, where Karel Šimánek and Tomáš Hubínek discussed their insights on AI and the evolving digital landscape. 

Our story shows how following your vision pays off, even when nobody else on the market sees it through.

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Business growth

We built the BigHub on a strong team, friendly culture, and meaningful work. These foundations have helped us to grow sustainably over the years, growing approximately 20+ percent each year. This year, we’ll reach the big milestone of CZK 100 million in revenue.

We’ve evolved from offering custom-tailored projects to scalable products that still allow for client customization. It’s about finding that sweet spot between prefabricated solutions and personalized service, like automating insurance claim processes to save time for our clients.

Our growth is based on the smart and motivated people we have the privilege to work with daily. Our team now consists of 46 amazing individuals. Whether it’s football matches, cycling trips, or summer parties, we succeeded in building and maintaining a culture that turns colleagues into lifelong friends.

We’ve always believed that education is key to staying ahead in such a fast-moving industry. That's why we not only work hard for our clients but also focus on our development. We regularly host internal workshops and hackathons, where our team can learn new upcoming technologies as well as come up with fresh ideas and innovative applications.

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Acknowledged Partners

None of this would be possible without the support of our great partners. Microsoft has been a key technology vendor for many of our enterprise clients, and our partnership with them continues to thrive as our business grows. With time our partnership deepened and we are proud to gain multiple statuses - Microsoft Managed Partner, Microsoft Solution Partner for Data & AI on Azure, or the latest Fabric Featured Partner, to name but a few.

As our client base has expanded, we’ve become technology agnostic, working seamlessly with all the major platforms and gaining their partnerships as well, including AWS, Google Cloud, and Databricks. These partnerships are just another proof of our team's hard work and their never-ending will to learn. At the same time, proficiency in all the major technologies helps us to pick and deliver the very best solutions to our clients.

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Giving back

Our connection to CTU – our alma mater – remains strong. For the past three years, we’ve been teaching a course on Applied Data Analysis, and this year we’re excited to launch a new follow-up course on Big Data tools and architecture. We sponsor student activities and provide both financial and non-financial support to initiatives like the Hlávka Foundation. We’re proud to say that our commitment to giving back remains unwavering. Whether it’s supporting the Data Talk community as a proud partner of the Data Mesh meetup for all the data enthusiasts out there, teaching, or mentoring students, we’re always looking for ways to inspire and contribute to the next generation of tech talent.

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BigHub beyond the big puddle 

This year we are strengthening our US presence! It represents a significant step forward in expanding our global reach, with a dedicated sales representative starting in October 2023 and multiple US clients already on board. It’s a bold move, and we couldn’t be more proud of the incredible team that made it happen. This is a big step for us in sharing our AI and data expertise with the largest market in the world!

As we look ahead, we’re more excited than ever for what the future holds and the new opportunities to innovate, grow, and give back. Our journey just started, and what a ride it has been so far!

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Pushing Boundaries with Generative AI

Artificial intelligence has always been in our DNA. Long before ChatGPT made headlines, we were using early GPT models for tasks like analyzing customer reviews. But the new wave of generative models has fundamentally changed what's possible. To keep up with — and get ahead of — this rapidly evolving space, we launched an internal initiative: BigHack, a hands-on hackathon focused on generative AI and its real-world applications.

We didn’t want to experiment in a vacuum. Instead, we chose real client challenges, making sure our teams were tackling scenarios that reflect the current business environment. The goal? Accelerate learning, build practical experience, and surface innovative AI-powered solutions for tangible problems.

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Secure and Scalable: Choosing the Right Platform

One key consideration was how to work with these models in a secure and scalable way. While OpenAI’s public services have sparked mainstream adoption, they come with serious concerns around data privacy and usage. Many corporations have responded by banning them entirely.

That’s why we opted for Microsoft’s Azure OpenAI Service. It allows us to leverage the same powerful models while ensuring enterprise-grade data governance. With Azure, all data remains within the client’s control and is not used for model training. Plus, setting up the infrastructure was faster than ever — minutes instead of days compared to older on-prem solutions.

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From Ideas to Prototypes: Two Real Projects

We selected two project ideas from a larger pool of discussions and got to work. With full team involvement and the power of the cloud, we built two working prototypes that could easily transition into production.

1. “Ask Your Data” App

This application enables business users to query analytical data using natural language, removing the dependency on data analysts for routine questions like:

• “What’s our churn rate?”
• “Where can we cut IT costs?”

By connecting directly to existing data sources, the app delivers answers in seconds, democratizing access to insights.

2. Intelligent Internal Knowledge Access

Our second prototype tackled unstructured internal documentation — especially regulatory and policy content. We built a system that allows employees to ask free-form questions and get accurate responses from dense, often chaotic documentation.

This solution is built on two key pillars:

• Automated Reporting: Eliminates the need for complex dashboard setups by using AI to interpret well-governed data and generate reports based on simple queries.
• Regulatory Q&A: Helps users instantly find information buried in hundreds of pages of internal compliance or legal documents.

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