AI Agents: 2025’s Biggest Love-Hate Relationship

AI agents have taken 2025 by storm, emerging as the hottest buzzword in tech. Businesses are rapidly adopting them, drawn to their ability to work autonomously, leverage external tools, and drive efficiency without human intervention.

These agents don’t take breaks, don’t get sick, and don’t demand raises – making them operational necessary. More importantly, companies aren’t just adopting AI agents for efficiency; they’re leveraging them to free up their workforce for higher-value tasks that demand human expertise.

Understanding AI agents: When to use them and when not to

AI agents are essential for tasks that require decision-making, memory, external data interaction, and continuous adaptation. They are particularly valuable for open-ended problems where the number of steps needed is unpredictable, making it impractical to hardcode a fixed path.

A strong example is automated market research and competitor analysis. Businesses tracking competitor pricing and product trends must dynamically gather, analyze, and process information from various sources, adjusting their approach as new data emerges.

However, many organizations fall into the trap of deploying AI agents for everything, assuming more automation equals better efficiency. This often leads to “AI agent sprawl” or “death by chatbot”—a chaotic environment where multiple agents operate redundantly, causing inefficiencies rather than solving them. For example, businesses may deploy AI agents for simple, predictable tasks like email sorting or FAQ handling, where a basic LLM would be far more effective.

Not every task requires an agentic approach. When a process follows a straightforward, pre-defined decision-making path, a Large Language Model (LLM) is the best fit. Deploying AI agents in such scenarios only adds unnecessary cost and complexity without any real benefit.

Here’s a table comparing LLM vs. Agentic AI:

Choosing between an LLM and an AI agent depends on the complexity of the task—LLMs are ideal for simple, static processes, while AI agents are essential for multi-step, dynamic automation.

The love side: Why AI agents are game-changers

The growing reliance on AI agents is fuelled by their ability to enhance productivity, automate repetitive tasks, and provide intelligent insights. Key benefits include:

• Multi-Step Decision Making & Strategic Actions
  AI agents can break down complex tasks into multiple steps, making real-time decisions at each stage. They automate entire workflows—such as IT troubleshooting or customer support—by diagnosing issues, retrieving logs, applying fixes, and escalating when needed.
• Integration with External Systems & Scalability
  Agents interact with APIs, databases, and third-party tools to fetch real-time data and execute tasks at scale. For example, an automated market research agent can pull real-time pricing data, analyze trends, and generate insights for strategic planning.
• Adaptability, Continuous Learning & Context Awareness
  Unlike rule-based automation, AI agents adapt based on new data, retain memory across interactions, and refine their logic over time. This is crucial in fraud detection, where agents analyze transaction patterns and evolve as fraud tactics change.
• Self-Correction, Error Handling & Autonomous Execution
  AI agents detect failures, retry actions, escalate issues, or take corrective steps automatically. This ensures workflows are completed with minimal errors and human intervention, making operations more reliable.

The hate side: The challenges of AI agents

Despite their advantages, AI agents also present several challenges that can hinder their adoption and effectiveness:

• Black-Box Decision-Making & Accountability: Many AI agents operate as “black boxes,” meaning their decision-making processes are difficult to interpret. This lack of transparency makes it challenging to understand why certain decisions were made, which raises concerns about accountability and regulatory compliance.
• Integration & Scalability Barriers: AI agents must integrate seamlessly with existing enterprise systems, but compatibility issues, API limitations, and rigid legacy infrastructures can create bottlenecks. Scaling AI agents across different platforms without disrupting workflows remains a significant challenge.
• Security & Privacy Risks: AI agents process vast amounts of sensitive data, making them potential targets for cyber threats. Ensuring robust security protocols and compliance with data privacy regulations is essential.
• Data Quality & Bias: AI agents rely heavily on the data they are trained on. Poor data quality or inherent biases in datasets can result in inaccurate, unfair, or even harmful decisions. Without rigorous data validation and governance, these issues can compromise trust and reliability.

This is where Databricks and zeb help businesses cut through the complexity of AI adoption. With our expertise and AI-driven solutions built on Databricks, organizations can deploy purpose-built agentic AI through a structured approach, ensuring efficiency, governance, and real business impact.

zeb’s approach: Multi-agent AI for self-service reporting

While working with a fintech client on a self-reporting system, our biggest challenge was the complexity of the underlying data. The system required intelligent handling of diverse data structures, optimized query execution, and seamless automation. A single-LLM approach would have been inefficient, error-prone, and difficult to scale. So, here’s how we helped the client to overcome these challenges.

Why we chose a multi-agent framework

Instead of relying on a single LLM to perform all tasks, we implemented a structured multi-agent system, each agent specializing in a specific function. This approach was critical to ensure accuracy and scalability:

• Orchestrator Agent – Acted as a central intelligence layer, dynamically delegating tasks to other AI agents, resolving dependencies, and optimizing execution efficiency.
• Visualization Agent – Integrated with Databricks BI dashboard to process and present key metrics, ensuring the system automatically selects the most effective visual representation of insights.
• Reporting Agent – Automated report generation and delivery, reducing manual effort and ensuring that reports were always accurate, timely, and customized to user needs.

Key practical benefits of implementing AI agents

This structured approach proved essential for handling the fintech client’s evolving data landscape.

• Accuracy & Context Awareness: The Query Agent leveraged chat history and data schemas to generate SQL queries that were both precise and contextually relevant.
• Automation & Intelligence: The Reporting Agent continuously adapted to changing data and user needs, automating the reporting workflow without manual intervention.
• Scalability: The multi-agent design allowed us to expand and modify components without disrupting the system, making it future proof.
• Efficiency: Specialized agents handled tasks in parallel, preventing system overload and improving response times.

Why Databricks is the backbone of AI agent enablement

Throughout our implementation, Databricks played a pivotal role in making AI agents more efficient, scalable, and intelligent. From data ingestion to query execution and visualization, every AI agent in our system was powered by Mosaic Agent, ensuring seamless automation and real-time decision-making.

Databricks’ Mosaic Agent is purpose-built to support the entire lifecycle of AI agents—development, orchestration, and deployment—within a fully integrated AI ecosystem. With the ability to dynamically interact with data, retrieve insights, and make autonomous decisions, Mosaic Agent empowers businesses to harness the power of AI-driven automation.

Why Mosaic AI is built for AI agents

Unified AI Framework: Mosaic AI seamlessly integrates LLMs, structured data, and ML models, enabling AI agents to process information across multiple modalities and extract meaningful insights from diverse data sources.

Agent-Centric AI Infrastructure: AI agents require real-time data access, context awareness, and memory to function effectively. Mosaic AI delivers this through intelligent data pipelines, advanced knowledge retrieval, and scalable workflows.

Scalable Model Deployment: AI agents must make decisions at scale, and Mosaic AI ensures seamless deployment of large-scale models with high availability, low latency, and continuous optimization—leveraging Databricks-native integrations for peak performance.

With Mosaic AI, businesses can build, deploy, and scale intelligent, adaptive AI agents that drive automation across industries. By bridging the gap between AI model intelligence and real-time execution, Databricks provides the ideal foundation for the next generation of AI agents.

Final thoughts

The AI agent revolution is here, but organizations must decide whether AI will be a strategic asset or an unmanageable burden. Databricks provides the Mosaic Agent framework, MLflow for traceability, Model Serving for real-time AI, and AI Gateway for governance, ensuring that AI agents work for the organization—not against it.

As a trusted Databricks Partner, zeb helps businesses streamline AI adoption by leveraging Databricks and Mosaic AI to build scalable, enterprise-grade AI solutions. With deep expertise in AI governance, data integration, and industry-specific applications, we ensure your AI agents drive real business value.