Agentic AI Frameworks to continue to watch out for in 2025

The evolution of Agentic AI is unlocking new frontiers in autonomous systems and intelligent agents. As we enter 2025, developers and enterprises are looking for frameworks that streamline AI workflows and enable complex decision-making and collaboration. In this blog post I will explore the top five Agentic AI frameworks that are set to transform the AI landscape in 2025.

1. Microsoft AutoGen: Orchestrating Multi-Agent Systems

https://www.microsoft.com/en-us/research/project/autogen/

Microsoft AutoGen is redefining the way we build autonomous, event-driven systems. This framework specializes in orchestrating multiple AI agents to solve complex problems in a distributed environment.

🔹 Key Features:

• Event-driven architecture for better scalability.
• Support for integrating agents with APIs and external tools.
• Advanced reasoning and task prioritization capabilities.

🔹 Why It Matters:
Microsoft AutoGen provides unparalleled support for creating systems that require multi-agent collaboration, making it ideal for use cases like IT infrastructure management and cloud automation.

🔹 Use Case:
Imagine an AI-driven cloud system that autonomously resolves server misconfigurations by delegating tasks to specialized agents or say you can envision a smart customer support system where AI agents collaborate to handle inquiries. One agent processes incoming queries, another retrieves relevant knowledge base articles, and a third escalates complex issues to human agents—ensuring fast, accurate, and seamless support.

🔹Notebook: https://github.com/microsoft/autogen?tab=readme-ov-file

2. LangChain: Building Seamless AI Workflows

https://blog.langchain.dev/how-to-design-an-agent-for-production/

LangChain has become a key player in workflow automation within the AI industry. It enables developers to link prompts, memory, and tools into cohesive pipelines, simplifying the process of building complex applications powered by large language models (LLMs). 

🔹 Key Features:- 

• Modular components for creating and managing workflows.
• Integrated memory for stateful applications. 
• Compatibility with multiple LLMs and APIs. 

🔹 Why It Matters: LangChain’s flexibility has made it a favorite among developers for building conversational agents, retrieval-augmented systems, and more. 

🔹 Use Case:  Creating a chatbot for a financial institution that can efficiently retrieve and summarize client data. 

🔹 Notebook:** https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/tutorials/agents.ipynb#scrollTo=17546ebb

3. LangGraph: Advanced State Management

https://blog.langchain.dev/langgraph-multi-agent-workflows/

LangGraph leverages graph-based architectures to manage stateful AI workflows. This framework is tailored for applications requiring dependency management and logical flow across multi-step tasks.

🔹 Key Features:

• Dependency graph-based workflow handling.
• Simplifies complex stateful systems with advanced logic.
• Flexible API integration for real-world applications.

🔹 Why It Matters: LangGraph is perfect for industries that require precise, sequential task execution, such as healthcare or supply chain management.

🔹 Use Case: Developing an AI agent for medical diagnosis that handles a multi-step process, from retrieving patient history to recommending treatments.

🔹Notebook: https://github.com/langchain-ai/langgraph

4. Microsoft Semantic Kernel: Bridging Semantics with Functionality

https://learn.microsoft.com/en-us/semantic-kernel/overview/

The Semantic Kernel by Microsoft focuses on contextual understanding and semantic reasoning. This framework is designed to merge the power of semantic AI with software development.

🔹 Key Features:

• Context-aware tools for real-world applications.
• Semantic reasoning to understand user intent better.
• Pre-built connectors for seamless integration into business systems.

🔹 Why It Matters:
By embedding semantic understanding into applications, Semantic Kernel improves decision-making accuracy in industries like customer service and IT operations.

🔹 Use Case:
Enhancing an IT helpdesk AI agent to understand user intent and execute commands efficiently.

🔹Notebook: https://github.com/microsoft/semantic-kernel

5. CrewAI: Multi-Agent Collaboration Made Easy

https://docs.crewai.com/introduction

CrewAI is paving the way for collaborative agent systems, focusing on task execution by multiple agents. With its emphasis on coordination and communication, CrewAI is perfect for building intelligent agentic ecosystems.

🔹 Key Features:

• Collaboration-focused design for agent teamwork.
• Built-in task planning and delegation mechanisms.
• Customizable modules for various industries.

🔹 Why It Matters:
CrewAI’s ability to handle multi-agent collaboration makes it a go-to framework for projects requiring team-based problem-solving, like logistics and resource planning.

🔹 Use Case:
Creating a fleet management system where agents collaboratively plan optimal delivery routes.

🔹Notebook:https://github.com/crewAIInc/crewAI

Why These Frameworks Matter

These frameworks are more than just tools — they’re blueprints for the future of Agentic AI. As businesses demand smarter, more autonomous systems, adopting the right framework will be critical for staying competitive in 2025

There are lot of Youtube videos from content creators that teach you how to create Agentic AI solutions take it further after you have browsed and learnt them and try making agentic AI systems that can be reused or chained together to solve complex workflows. 

Cheers, 

Missional & Cultural Fit vis vis Technical and Team Fit. - Random thoughts on hiring and designing interviewing templates

 

In my leadership and consulting roles with various organizations over my tenures, I am sometimes amused, shocked, and not the least surprised by the lack of structured interviewing methodologies hiring managers or director-level leadership uses. Over the years, I have developed a simple template for non-profits that could be easily tweaked and replicated for a profit organization equally. The idea is to have interview panelists divide the questions on the interview questions among themselves and have each member of the interview team score all questions irrespective of who is asking the question. The idea is to have some structure (so panelists are not struggling to ask questions or the right questions) to get to know the prospective candidate to capture observations and score each question answered. 

The template assesses the candidate in four areas: Missional Fit, Team Fit, Cultural Fit, and Technical Fit. 

After the interview, the panelists collectively review their scores and discuss. This allows them to understand each other's scoring rationale and share qualitative observations, fostering a collaborative and unified decision-making process.  It is recommended that someone from one or two departments' key leaders or stakeholders also participate with the hiring manager and the panel interviewing the candidate who has applied. This provides an interesting vantage point for observations and scoring, allowing us to collaborate more and overcome biases as much as possible. 

In the past, it's incredible how different scores and qualitative observations from various team members give the hiring manager different perspectives and foster a sense of unity that is part of transformative cultures. This simple tool can easily be plotted on a four-quadrant or two-quadrant x-y model, with the x-axis measuring missional and character scores and the y-axis plotting Team fit and Technical fit components. We have scores plotting more to the upper right-hand corner, and the cluster of new hires who form the team increases. You are on your way to building great teams that have closer congruency within your team and your own organizational cultures. 

I have noticed there are times when a prospective candidate may not be a very high missional fit but scores well on the other three vectors, and this is where managerial and leadership decision-making becomes an art rather than a science where discernment, unity, and collaborative discussions enable you to come to sound decisions. It is an opportunity to gauge if a prospective candidate (internal incumbent) or an external candidate can provide opportunities and challenges in providing mentorship, coaching, and encouragement or allowing the team to pause before making an offer. The opposite could also be true if the individual exhibits a strong missional and cultural fit but lacks the core competency of technical acumen needed along with being a team fit (jelling well with the team they are going to be on)- discussion between the panelists of pros and cons will allow them to come to a place of unity and sound decision-making. 

After all, you, as a leader, are only strong if you have a great team and team members who support your organization's vision, mission, and strategies. 

Guessing what's going to be new in AI for 2025

 AI has been the buzz technology for the last two or three years in the main stream with LLM's like chatgpt and a host of variants that flooded our market but as I like to guess/foresee where this buzz and real progress is going to take its kinda of fun based on reading to collate some thoughts here.  I will categorize this into three broad strokes. Large language models that have some kind of reasoning capability, ability to create virtual playgrounds with context and AI applications in scientific research. 

Large Language models Becoming more intelligent (able to reason ...kind of) 

Here’s where things get even more interesting. When OpenAI dropped o1 in September, it didn’t just introduce a new language model; it sparked a paradigm shift. Two months later, o3 took things further, pushing boundaries we didn’t even know existed. Unlike traditional models like GPT-4, which spit out answers as they come to mind—sometimes right, sometimes wrong—these new models are designed to think through their responses. They break down complex problems into manageable steps, trying one approach after another until they get it right. This so-called “reasoning” capability (we know, the term is loaded) is game-changing for accuracy, especially in math, physics, and logic. And let’s be honest, it’s a crucial leap for AI agents. What I like about this in practical applications is when chatbots to whatever agentic AI models we come up with are able to build on context and add that context to some sort of learning model and then keep building that knowledge-base in the background. 

Take for example  Sal Khan in his TED talk demonstrated this with the next evolution of Khan academy

Scenario: A student struggling with calculus uses an AI tutor for help. Older models provide step-by-step solutions to specific problems but don't adapt well to different learning styles or gaps in foundational knowledge.

With Reasoning AI: The AI identifies the student’s weak points, breaking complex problems into smaller, manageable steps while adjusting explanations to match their understanding. If one explanation doesn’t resonate, the AI tries another approach, like visualizing the problem with graphs or interactive models. Sal suggests that Khan Academy will have the ability like a personal tutor to carry a conversation with a student and make suggestions that help in thinking about problems and reason through it with AI chat augumenting it. 

Outcome: More effective learning tailored to individual needs.

AI Is Booming or a Boon to Boost Science

 AI is accelerating scientific discovery. Last October Nobel Prize winners in Chemistry to Demis Hassabis and John M. Jumper of Google DeepMind for their work on AlphaFold,  cracked the protein-folding problem, and to David Baker for tools that design entirely new proteins. This wasn’t just a win for AI; it was a massive step forward for humanity. So based on this trend in natual sciences so expect 2025 to bring a surge of data sets and models targeted at unlocking the mysteries of the natural world. Proteins were just the beginning because they had the perfect data sets for training AI. The hunt is on for the next big breakthrough—and it’s anyone’s guess what that will be.

Virtual Playgorunds 

If 2023 was all about generative images and 2024 brought us generative video, what’s the next frontier? You guessed it—generative virtual worlds, aka video games. And this isn’t some far-off dream; the groundwork is already being laid. Back in February, Google DeepMind gave us a sneak peek of what’s possible with their generative model, Genie, which turned a single still image into an interactive 2D side-scrolling game. Then in December, they upped the ante with Genie 2—a model capable of transforming a starter image into an entire interactive virtual world. And it’s not just Google. Other companies are racing to develop similar technology. 2025 could be the year where the line between gaming and AI artistry blurs completely.

Last February Genie 1 showed us the ability to transform a single still image into an interactive 2D side-scrolling game, similar to classic platformers like Mario Bros. This innovation allowed users to upload any image and watch it evolve into a playable environment, complete with logic and physics rules generated by AI. In December: Genie 2 expanded this capability by creating fully immersive virtual worlds from a starter image, enabling more complex environments, characters, and storylines. Imagine sketching a tree, and the AI generates an entire forest biome with interactive flora, fauna, and unique weather patterns. 

Another good example will  be Sony AI and Procedural Storytelling

• The Vision: Imagine a PlayStation game where players make decisions, and AI generates the story dynamically. Instead of pre-written narratives, every player experiences a unique storyline, shaped by their choices and the AI’s ability to adapt in real time.
• Practical Example: A fantasy RPG where your interactions with NPCs generate unique quests, alliances, and outcomes that vary dramatically from other players’ experiences.

Mojang Studios brings generative AI into Minecraft, and suddenly, building your dream world is as simple as describing it. I could just say, “Create a volcanic island with a hidden temple and treasure,” and—like magic—the game would bring it to life in real-time. Picture a fully realized environment, complete with fiery lava flows, intricate challenges, and treasures waiting to be uncovered. No painstaking block-by-block construction, just pure creativity unleashed.

2025 isn’t just another year for AI; it’s shaping up to be a year of extraordinary leaps. So, buckle up—the future’s unfolding faster than we ever imagined.

Data Insights - Setting SMART Goals

As a habit for continuous improvement, I want to return to this space and keep posting. Last year was very busy, hence the massive gap in not recording and writing. 

Mastering the ability to harness and interpret data is essential for success in any organization. In this post, I want to oversimplify SMART goals for my own sake to develop clear, data-driven goals that position you as the go-to person for transforming data into actionable strategies. The famous SMART Goal framework, which stands for Specific, Measurable, Achievable, Relevant, and Time-bound, will help to achieve this. Lately, I have been thinking about how an email campaign goal would be defined for one of our product's marketing goals. Let's use it as a running example to illustrate improving email marketing performance.

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1. Be Specific

Specific goals provide a clear direction and eliminate ambiguity. Define your objectives with precision so everyone involved understands what's expected. For example, instead of a vague goal like "improve email performance," a specific goal would be:
"Increase the email open rate by 15% in the next quarter by refining subject lines and targeting segmented audiences."

This clarity ensures alignment across your team and integrates the goal into the larger business context.

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2. Make Goals Measurable

Measurable outcomes are the heart of any data-driven strategy. Without clear metrics, decision-making becomes guesswork. In email marketing, measurable metrics include open rates, click-through rates, and conversions.
"Track open rates weekly, aiming for an increase from 20% to 35%, and set a benchmark click-through rate of 5%."
This ensures you can monitor progress and refine strategies as needed.

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3. Set Achievable Goals

While ambition is valuable, goals must remain realistic. Unrealistic targets can lead to frustration and missed opportunities. For example, if your current open rate is 20%, aiming for 50% in just one month might not be feasible.
"Set an achievable target of increasing open rates to 35% over the next three months by implementing A/B testing and optimizing email timing."
This approach provides a reasonable target while encouraging steady improvement.

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4. Focus on Relevance

Your goals must align with your organization's priorities. Ask yourself, "How does this goal support the business's growth or objectives?" In our example, improved email performance is directly relevant if the organization relies on email campaigns for lead generation or customer engagement.
"Focus on improving open rates because higher engagement will drive more traffic to our website, supporting our goal of increasing sales conversions by 10% this quarter."
This ensures the goal contributes meaningfully to broader business objectives.

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5. Make Goals Time-Bound

Practical goals require deadlines. A time-bound goal ensures accountability and keeps efforts aligned with organizational priorities. For the email marketing example, set a specific timeline:
"Achieve a 15% increase in open rates and a 5% click-through rate by the end of the next quarter (90 days)."
This creates a clear deadline to measure progress and maintain momentum.

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By following the SMART framework and applying it to actionable examples like email marketing, you can create measurable, realistic, and impactful strategies that align with your organization's goals and drive success.