10+ Agentic AI Trends and Examples for 2025

The future of agentic AI isn’t just about improving tools or streamlining business workflows. It’s about integrating AI deeply and transforming business approaches by restructuring current frameworks. 

10+ agentic AI trends and examples

1. Towards autonomous, self-healing data pipelines

As organizations scale their AI and analytics initiatives, maintaining high data quality across pipelines becomes increasingly complex. Traditional approaches like adding manual checks, patching pipelines reactively, or scaling data engineering teams may become difficult to scale.

Instead of relying on human-driven monitoring and repairs, future data pipelines will be embedded with AI agents with reinforcement learning and modular architectures that can:

• Monitor pipeline health and identify problems early, using observability and metadata.
• Diagnose root causes (e.g., schema drift, missing data, delayed upstream feeds).
• Autonomously repair issues (e.g., roll back to last good configuration, re-ingest failed batches, or dynamically adjust transformations).

Real-world examples:

• Companies like Monte Carlo are developing “data observability” platforms to give AI agents a full view of how the pipeline works.
• Enhancing CI/CD Pipelines with agentic AI: Research into autonomous MLOps pipelines (e.g., self-healing feature stores) is accelerating.

The traditional debate of “process vs. tooling” is becoming less relevant with the rise of agentic AI. 

While strong processes are still important, agentic AI tools, which autonomously plan, decide, and execute multi-step tasks, are starting to replace the need for complex process design in some areas.

• Self-directed agents can automate workflows end-to-end without requiring users to manually manage every step.
• Non-technical users can now deploy automations (e.g., data pipeline management, cybersecurity threat hunting) without deep expertise.

In effect, agentic tools are shifting the conversation: instead of optimizing the process around human teams, the tools become the new operational model. 

Over the next years, enterprises may shift away from isolated tools toward full-process, operationalized agentic AI solutions. 

3. Vertical AI agents in specialized industries

There is a shift from general-purpose foundation models (like ChatGPT) to more specialized AI agents (like Cursor AI code editor). This shift towards narrow agents is built for specific roles and offers key advantages to streamline business operations, including:

• Higher accuracy in industry-specific tasks.
• Improved efficiency through automation of domain-specific workflows.
• Deep integration into business systems for tailored solutions.

Examples of vertical AI agents:

• AI agents in customer service: Respond to queries in natural language, interpret context, and generate human-like responses.
• AI agents in healthcare: Automate healthcare processes, execute several business tasks such as medical coding, appointment scheduling, and office administration.
• AI agents as developers: Automate code suggestions, debugging, and software testing.
• AI agents as computer users: Automate everyday tasks like reminders and security monitoring.
• AI QA testers: Automated software testing systems.

4. Integration of AI agents with the physical world

AI agents increasingly integrate more deeply with Internet of Things (IoT) devices and the physical world. Applications span various environments, including smart homes, offices, and cities, where AI agents autonomously control devices.

Real world example:

Tech companies like NVIDIA and GE HealthCare are already working together on agentic robotic systems like X-ray and ultrasound technologies, where AI agents use medical imaging to interact with the physical world.

5. Growing shift towards open source models

For years, proprietary AI models controlled by a few large tech companies dominated the landscape. But this is quickly changing with open source models like Anthropic and Mistral.

• For B2B (business-to-business) companies, open-source models are favored due to their lower operational costs. This is especially true for smaller models that are often sufficient for specific, well-defined tasks. Companies can fine-tune AI models in-house, reducing reliance on costly third-party APIs.
• For developers, smaller, open-source models can be fine-tuned to specific business functions or domains, 

Proprietary models response: OpenAI strives to make its models more accessible. Models like ChatGPT have already cut prices by roughly 50%. They charge us around $5 per million tokens for inputs and $10 per million tokens for outputs. Onboarding a product used to cost us 50 cents.

6. Convergence of AI capabilities in agentic systems

While traditional AI frameworks divide functionality into prediction, search, and reasoning, real-world agentic AI systems are already blurring these lines. 

Autonomous agents will operate as holistic multi-agent systems (MAS) and seamlessly integrate all three capabilities simultaneously to operate effectively in dynamic environments. 

Real-world examples:

• Autonomous vehicles (e.g., Waymo, Tesla FSD)
• Warehouse robots (e.g., Amazon Robotics)
• Healthcare diagnostic agents (e.g., Google DeepMind’s MedPaLM)

7. AI agent building frameworks

We have seen the rise of many AI agent building frameworks like OpenAI Swarm, LangGraph, Microsoft Autogen, CrewAI, Vertex AI, and Langflow. The frameworks offer pre-packaged tools and templates that enable the development of AI agents tailored for various use cases.

AI agent building frameworks enabled users to expand their use cases by allowing:

• LLM integration: Selecting LLMs like OpenAI, Anthropic, or Mistral to create specialized agents for your needs.
• Knowledge base integration: Integrate custom documents (json, PDFs, websites) for improved accuracy and relevance.
• Built-in memory management: Automatically track conversation histories for personalized interactions.
• Custom tool integration: Allow agents to perform tasks like payments, web searches, and API calls.

8. Combining synthetic and real-world data

Companies are increasingly combining synthetic and real-world data to train their AI models effectively. 

While real-world data offers valuable insights, it often faces limitations such as scarcity, privacy concerns, and inherent biases. Synthetic data, however, provides a controlled environment where AI can be trained on diverse scenarios.

Real-world examples:

• Companies like Waymo use synthetic data to simulate these rare events, which are then integrated with real-world driving data to train their AI models.
• NVIDIA creates synthetic environments to train robotic agents for physical-world tasks like factory automation and autonomous surgery assistance.

9. Agentic AI reshaping team roles

Agentic AI redefines how responsibilities are distributed between analysts and engineers. Teams are taking on expanded responsibilities. Analysts are being empowered to build and manage pipelines, while engineers increasingly automate core workflows. 

Two major forces are driving this shift:

• Advances in AI-enabled pipeline automation: Agentic systems can autonomously handle multi-step workflows such as data ingestion, validation, and incident detection. As automation advances, engineers can manage larger systems with fewer resources, while analysts independently maintain workflows.
• Increased demand for AI and data products: As business leaders seek faster and broader access to data, teams are expected to do more with fewer resources. Analysts are taking more technical tasks, while engineers focus on scaling and automating infrastructure.

10. The human element in agentic AI

The true success of agentic AI depends largely on how well humans can integrate and use these systems. 

Key points:

• Human-AI collaboration: The effectiveness of agentic AI will rely on how effectively teams can collaborate with AI agents, using them as co-workers.
• Cultural shift: Adopting agentic AI will require a significant shift in organizational culture, focusing not just on technology adoption but also on allowing people to work alongside AI to reach new heights of productivity.

11. Emergence of new AI agent pricing models

The adoption of digital co-workers might reshape how businesses value tasks traditionally performed by humans. 

This transition is driving the rise of agentic business models that favor salary-based compensation over conventional software licensing structures.

Real world example:

Hippocratic AI’s agentic nurses, which are priced at $10 per hour, are lower than the median hourly wage of ~$43 for human registered nurses.

For more: AI agent pricing.

Agentic AI explained

Agentic AI refers to AI systems capable of acting autonomously, adapting in real-time, and solving complex multi-step problems based on context and objectives. 

It combines multiple AI agents, leveraging large language models (LLMs) and reasoning capabilities.

Key features:

• Autonomous decision-making: Acts independently with minimal human intervention.
• Real-time adaptation: Adjusts to changing circumstances and evolving situations.
• Multi-agent collaboration: Multiple agents work together to solve complex problems.
• Reasoning: Uses reasoning and natural language understanding to process and respond to challenges.

Read more: Levels of agentic systems.

Agentic AI vs generative AI

Generative AI generates content (text, images, etc.) based on input data or prompts.. It uses deep learning models that mimic the human brain’s learning and decision-making processes. 

By analyzing large datasets, these models identify patterns and generate content in response to natural language requests, relying on technologies like robotic process automation (RPA).

Agentic AI refers to AI systems that autonomously make decisions and act towards achieving complex goals with minimal supervision. It combines the flexibility of large language models (LLMs) with the precision of traditional programming. 

Unlike generative AI, which is reactive to input, agentic AI proactively adapts to situations and makes context-based decisions. It’s used in applications like robotics, complex analysis, and virtual assistants.

The impact of AI agents on business growth

Capgemini claims that ~80% of surveyed organizations plan to integrate AI agents within 1-3 years for tasks like email generation, coding, and data analysis.

However, the true impact will come from orchestrating these agents to complete not only individual tasks but also entire software development lifecycles.

Companies could deploy specialized agents for code generation or automated testing, all working together and adjusting in real-time based on human feedback.

More generally,  AI agents can simplify the automation of complex use cases in four ways:

• Adaptability to unpredictable scenarios: Unlike deterministic rule-based systems that fail when faced with unexpected conditions, AI agents trained on large datasets can respond flexibly to unforeseen situations.
• Use of natural language for workflow automation: Agentic tools allow users to design and modify workflows using natural language instructions.
• Autonomous integration with existing systems: Since agentic tools are built on foundation models, AI agents can autonomously interact with software platforms and tools.
• Continuous learning and improvement: Agentic tools can continually learn from interactions and feedback, improving their performance without needing extensive manual retraining.

Further reading

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