Agentic AI: Part 4 — Agentic AI Frameworks
Introduction to Agentic AI Frameworks
In Part 3 of this series, we unpacked the key terms that shape how we talk about Agentic AI. But words alone don’t deliver value, they’re just the vocabulary of the field. What truly brings those ideas to life are frameworks.
Frameworks are the bridge between concept and execution. They give agents the structure to plan, reason, and collaborate much like an organisational chart gives order to a business. Without them, agents remain isolated programs with limited scope. With them, they evolve into coordinated systems that can scale and function like true digital co-workers.
In this article, we’ll look at what makes up an Agentic AI framework, why they matter, and how the leading frameworks differ in design and application.
Why Frameworks Matter
Before diving into the components, it’s worth pausing to ask: why do we need frameworks at all?
Large language models (LLMs) are powerful, but left on their own they are like highly talented freelancers without a manager. They can draft content, summarise documents, or answer questions, but they lack coordination. Give them multi-step tasks that require memory, collaboration, or rule-following, and they quickly fall short.
Frameworks step in to provide:
- Structure: Defining who does what, in what order, and how tasks are handed off.
- Reliability: Ensuring workflows run consistently, even when inputs are messy.
- Scalability: Making it possible to manage dozens or hundreds of agents working in concert.
- Transparency: Offering traceability, guardrails, and accountability.
In other words, frameworks turn raw AI horsepower into organised, productive systems.
The Core Components of Agentic AI Frameworks
Think of an Agentic AI framework as the anatomy of a digital workplace. Each part contributes to the whole, and together they allow agents to function as more than the sum of their parts.
Agents: The Roles
Agents are specialised workers in the system. Imagine an insurance company:
- A Triage Agent sorts incoming submissions.
- A Pricing Agent calculates premiums.
- A Compliance Agent ensures regulatory checks.
Just like human teams, each agent has a defined role.
Interaction: The Conversations
Agents don’t work in isolation. They pass tasks to each other much like teammates in a project. For instance, a data extraction agent might pull customer details from a PDF, and then a risk assessment agent uses that data to calculate exposure.
Agent-as-a-Service: The APIs
Some agents are packaged as services, accessible via APIs. A broker might call a Quote Agent through an API to instantly generate a policy quote. This “as-a-service” design makes agents reusable across applications.
Frameworks: The Structure
If agents are workers, then frameworks are the org chart. They define who does what, how tasks are delegated, and how workflows are orchestrated.
AI Models: The Brains
Frameworks provide structure, but agents still need intelligence. That comes from AI models:
- Language models for reading documents.
- Embedding models for pattern recognition.
- Guardrails to ensure safety and compliance.
Memory: The Notebook
Just like humans rely on short- and long-term memory, agents need memory too:
- Short-term memory holds the current conversation context.
- Long-term memory stores customer histories or prior interactions.
- Episodic memory recalls specific past events.
This memory helps agents provide continuity and personalisation.
Applications: The Workplace
All these pieces come together in real-world applications: customer service bots, underwriting workflows, or knowledge extraction pipelines.
Open Issues: The Challenges
Of course, challenges remain. Scaling these systems, ensuring safety, and integrating across enterprise systems are still open problems.
To summarise, image pPicture a modern office. Each employee (agent) has a role. They collaborate (interaction), use shared tools (AI models), follow a reporting structure (framework), and keep notes (memory).
Together, they achieve goals that no individual could complete alone.
That’s what Agentic AI frameworks aim to replicate.
Popular Frameworks: First Look
Now that we know the building blocks, let’s review the frameworks that bring Agentic AI to life.
Agentic AI frameworks form the foundation for systems where AI agents can act with autonomy, context-awareness, and defined goals. They are powered by large language models, giving agents the ability to interpret tasks, use tools, and adapt in real time.
Unlike traditional approaches, these frameworks are not one-size-fits-all.
Each design choice shapes how agents reason, collaborate, and execute tasks. By classifying frameworks by their principles and patterns, we gain clarity on how they influence agent behavior and coordination. This makes it easier to match the right framework with the right problem, whether in insurance, finance, or customer support.
Agentic AI frameworks generally fall into four categories: orchestration and multi-agent workflows for coordinating teams of agents, lightweight frameworks for quick prototyping, enterprise orchestration tools for scale and governance, and data-centric frameworks for knowledge retrieval and reasoning.
Each type serves different needs, from experimentation to enterprise-grade automation.
#1: Orchestration & Multi-Agent Workflows
These frameworks are designed to manage teams of AI agents working together, just like people in a project team with clear roles and responsibilities. They focus on coordination, delegation, and smooth hand-offs, making them essential for solving complex, multi-step problems.
AutoGen (Microsoft)
AutoGen is Microsoft’s open-source framework built for designing multi-agent ecosystems. Unlike single-agent tools, it enables structured communication between multiple agents that can negotiate, delegate, and share context. This makes it useful for workflows such as research, customer support, or document automation where no single agent can handle the full complexity alone.
CrewAI
CrewAI takes inspiration from human teams by assigning agents defined roles planner, analyst, executor and organizing them into “crews.” These crews follow structured collaboration patterns, making workflows transparent and repeatable. With templates, connectors, and visual tools, CrewAI lowers the barrier for both developers and business users to experiment with team-based agent designs.
MetaGPT
MetaGPT mirrors organisational structures found in software or product development. Agents are cast as project managers, developers, and testers, and work together in sequential steps. By codifying organisational workflows, MetaGPT is especially strong for building consistent, multi-stage outputs such as product blueprints, codebases, or test plans, reducing duplication and human coordination overhead.
#2: Lightweight Frameworks
Lightweight frameworks are built for speed and simplicity, enabling quick setups for chaining prompts and tools. They strip away unnecessary complexity, making it easy to experiment, prototype, and test ideas with minimal overhead. These frameworks are ideal for developers who want fast iteration cycles and straightforward integrations, making them a go-to choice for smaller tasks, proof-of-concepts, and low-barrier entry into agent development.
SmolAgents
SmolAgents focuses on simplicity and speed, offering a minimal yet flexible way to build agents. With just a few lines of code, developers can chain prompts and tools to test new ideas. This makes it ideal for rapid prototyping, hackathons, and classroom teaching where ease of use matters more than enterprise features.
PydanticAI
PydanticAI builds on the trusted Pydantic library, enforcing data schemas for every input and output. This guarantees consistency even with unstructured or messy data. By combining validation with orchestration, it ensures that agents behave predictably an essential requirement in regulated industries like finance or healthcare where accuracy and reproducibility are non-negotiable.
CopilotKit
CopilotKit embeds AI agents directly into apps, transforming them into in-app copilots that understand user intent and application context. Its two agent types Concierges and Workers, make it flexible enough for everything from customer support features to developer assistants. By integrating with app state, CopilotKit enables AI support without needing external orchestration layers.
#3: Enterprise & Orchestration Tools
Enterprise and orchestration tools are designed for scale, reliability, and integration. They provide the infrastructure needed to manage complex, multi-step workflows across business systems, while ensuring compliance, observability, and security. These tools go beyond simple experimentation, offering features like memory, monitoring, lifecycle management, and enterprise-grade governance. They are ideal for organizations that need to embed AI agents into mission-critical processes, automate at scale, and integrate seamlessly with existing platforms and data sources. By combining orchestration with enterprise controls, these tools enable AI adoption in regulated and large-scale environments where consistency and trust are paramount.
OpenAI Agents SDK
The OpenAI Agents SDK is a developer-oriented toolkit that simplifies building both single and multi-agent systems. It provides high-level building blocks — memory, tool usage, and guardrails — so teams can focus on workflows rather than plumbing. With built-in observability and integration into OpenAI models, it is well suited for enterprise-grade, production-ready deployments.
LangGraph
LangGraph organises multi-agent workflows as graphs of nodes and edges. This gives developers precise control over branching, recombining, and maintaining state across long-running processes. Its graph-based structure improves transparency and reproducibility, making it attractive for enterprises and researchers that need audit trails, modularity, and robust orchestration for complex business workflows.
Semantic Kernel (Microsoft)
Semantic Kernel combines AI reasoning with conventional programming. It allows developers to create workflows blending language models, plugins, and deterministic code in languages like C#, Python, and Java. By supporting connectors to enterprise systems, it helps organizations gradually embed AI into production systems while maintaining reliability, explainability, and fine-grained control.
Agno
Agno is a declarative agent framework that emphasises explainability and transparency. Developers can specify agent goals, tools, and reasoning logic in structured form, making agent decisions traceable and auditable. This accountability makes it particularly suitable for regulated sectors such as insurance, healthcare, and finance, where organizations must justify and monitor AI-driven processes.
#4: Data-Centric & Distributed Frameworks
Data-centric and distributed frameworks are built to give AI agents access to knowledge at scale while ensuring collaboration across multiple systems. They focus on enabling agents to retrieve, process, and reason over large volumes of structured and unstructured data, making them particularly effective in knowledge-intensive tasks. These frameworks often include features like retrieval-augmented generation (RAG), distributed orchestration, and connectors to diverse data sources. They are ideal for organisations that need agents to work across silos, tap into enterprise data lakes, or integrate with external information streams. By prioritising scalability, interoperability, and data access, these frameworks allow agents to deliver accurate, context-rich results in environments where information is vast, distributed, and constantly evolving.
LlamaIndex
LlamaIndex equips agents with access to both structured and unstructured data, from PDFs to APIs and databases. Its strength lies in retrieval-augmented generation (RAG), where agents ground reasoning in relevant, up-to-date knowledge. By offering indexing tools and connectors, LlamaIndex supports knowledge-intensive tasks such as enterprise search, risk assessment, or customer onboarding.
Google ADK
Google’s Agent Development Kit (ADK) is an experimental framework for scalable, distributed agent systems. Unlike isolated frameworks, ADK focuses on interoperability, enabling agents to discover, collaborate, and coordinate across diverse platforms. By supporting protocols like Agent-to-Agent (A2A) and MCP, it positions itself as a foundation for future cross-system, enterprise-scale AI ecosystems.
Comparison of Agentic AI Frameworks
Conclusion
Agentic AI frameworks are more than just new tools, they’re the foundation for a new way of working with AI. Instead of static assistants, these frameworks enable agents to act with autonomy, memory, and collaboration, making them true digital co-workers.
From lightweight tools like SmolAgents for rapid prototyping to enterprise-scale solutions like LangGraph or OpenAI’s Agents SDK, the ecosystem is evolving quickly. Choosing the right framework depends on your goals: are you experimenting, scaling, or integrating into mission-critical processes?
One thing is clear: Agentic AI frameworks are set to become as essential as cloud platforms were in the last decade. Organisations that start exploring today will be better positioned to lead tomorrow.
