The internet has a new obsession, and it goes by two words: Agentic AI. It is on every headline, every hot take, every pitch deck slide number three, and somehow still under-explained.
In the first Episode of the Agentic AI series, we covered what agentic AI is and why it is changing how businesses operate. Now it is time to peel back another layer and look at how these systems actually work — how agentic workflows are structured, what the top five agentic AI frameworks are, and how your business can choose the right one.
Quick recap: Agentic AI systems can set sub-goals, use tools, coordinate multiple AI agents, and complete complex multi-step workflows with minimal human input. If you haven’t read Episode 1 yet, start here first.
What’s Really Going On Inside an Agentic AI System?
Every agentic AI system, regardless of the framework on which it is built, rests on five core components. You do not need to understand the engineering behind each one to make good decisions. But knowing what each component does will help you evaluate agentic AI frameworks intelligently, catch architectural mistakes early, and communicate clearly with the people building these systems.
1. Goal Decomposition and Planning
When a system receives a complex goal, the first thing it does is break it down. A planner agent — often called the ‘orchestrator’ — takes a goal, for example “Research our top three competitors and draft a pricing comparison for the board deck” and maps it into a sequence of concrete sub-tasks: search their pricing pages, extract the key data points, compare against our own pricing, identify the gaps, draft the summary, validate the figures.
This decomposition step is what makes agentic workflows fundamentally different from automation. Automation requires a human to define every step. Agentic AI plans the steps itself. That is the shift: from humans doing the thinking and AI doing the execution, to AI doing both.
Importantly, this planning is not rigid. If a step fails or produces unexpected results, the orchestrator can replan. It can skip a step, add a new one, or change the order. This flexibility is what allows agentic systems to handle real-world tasks, which almost never go exactly as predicted.
Next up, to actually carry out these plans, the agent needs memory, both short-term and long-term.
2. Memory: Short-Term and Long-Term
Agents need to remember things. Without memory, every interaction starts from scratch, and the agent cannot build on previous work. Agentic systems use two kinds of memory.
Short-term memory covers the current session.
What is the goal? What steps have been completed? What did each action return? What is the current state of the draft? This memory lives in the agent’s context window (the amount of text it can actively hold in mind during a task).
Long-term memory covers knowledge that persists between sessions. Your company’s products, past project outputs, customer history, regulatory rules, and brand guidelines. Most production systems store this in a vector database, such as Pinecone, Weaviate, or Chroma. This means agents retrieve information by matching context rather than akeyword.
To clarify: a regular database stores information and finds it by matching exact words. You search for “Q2 revenue,” and it looks for those exact words. If the document says “second quarter earnings” instead, it finds nothing.
A vector database works differently. It converts every piece of information — a document, a paragraph, a customer note — into a string of numbers that represents its meaning. These numbers are called vectors. When you search, it does not look for matching words. It looks for matching meaning.
So if you ask “what did we earn in Q2?” it finds “second quarter earnings report” — because the meaning is close, even though the words are different.
This long-term memory is what transforms an agent from a one-off tool into something that gets more capable over time as it accumulates knowledge about your business.
3. Tool Use: The Action Layer
An agent without tools is just a text generator. Tools are what give agents the ability to act in the real world, not just produce words about it. A tool is any capability the agent can call upon, such as searching the web, reading and writing files, executing Python code, querying a database, calling third-party APIs like Salesforce or HubSpot, sending emails, booking calendar events, or submitting forms.
IBM states that agents use function-calling interfaces to connect to these tools, filling knowledge gaps and taking real actions that go beyond generating language. The practical capability of an agentic system scales directly with the quality and breadth of its tool library. A well-tooled agent can handle end-to-end agentic workflows that would previously have required multiple software systems and human coordination. A poorly-tooled agent, no matter how sophisticated its reasoning, cannot accomplish much.
When evaluating what an agentic AI system can do for your business, the first question to ask is: what tools does it have access to? That answer tells you more about its practical capability than any benchmark score.
4. Multi-Agent Coordination
For complex goals, a single agent is rarely enough. Real agentic systems use specialist agents working together: one researches, one writes, one validates facts, one executes actions in external systems. An orchestrator manages the whole team, deciding who does what and when, passing information between agents, and tracking overall progress toward the goal.
Gartner reported a 1,445% surge in enterprise inquiries about multi-agent systems between Q1 2024 and Q2 2025. The reason is practical: just as a company does not hire one generalist to run its entire marketing function, agentic systems deliver their greatest value when specialist agents each do what they are best at, coordinated by a manager who keeps the team aligned toward the shared goal.
This coordination is also where most of the interesting engineering challenges live. Agents can produce contradictory outputs. One agent might not have the context that another agent has already gathered. An orchestrator might assign a task to the wrong specialist. The agentic AI frameworks covered later in this article exist largely to reliably solve these coordination problems.
5. Feedback and Self-Correction
After every action, the agent evaluates the result. Did it work? Did it get the agent closer to the goal? If a search returns unhelpful results, it tries a different query. If the code produces an error, it reads the error and rewrites it. If a validator agent flags a data point as uncertain, the research agent returns to verify it. This feedback loop runs continuously throughout a task.
This is also the component that makes production reliability so important to get right. A feedback loop without clear exit conditions can run indefinitely, consuming compute and cost with nothing to show for it. Frameworks with strong observability — the ability to trace and inspect every loop, every tool call, every agent decision — are essential for catching these runaway scenarios before they become expensive problems.
Best Agentic AI Frameworks in 2026
A framework is what stops you from rebuilding the same infrastructure every time you want to add an agent to your stack. It gives you pre-built components for memory management, agent communication, tool integration, error handling, and orchestration logic. Without one, building even a two-agent system from scratch takes months of engineering. With the right agentic AI framework, a small team ships a working prototype in days.
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1. LangChain and LangGraph
LangChain launched in 2022 and became the most widely adopted framework in the ecosystem, with roughly 63% adoption among teams building LLM applications. To understand what is LangChain at its core, it provides modular components for chaining LLM calls, connecting tools, managing memory, and building agent workflows. But LangChain’s own team recognised the limitation — linear chains are too rigid for real agentic workflows. Real tasks branch, loop, and require conditional logic. A research agent might need to search again if the first result is irrelevant. A validator might reject output and trigger a rewrite. Linear chains cannot model this cleanly.
That is why they built LangGraph — a graph-based orchestration layer where each node in the workflow is an agent, tool call, or decision point, and the edges between nodes define how data and control flow. This lets you model branching logic, loops, parallel execution, and conditional paths with precision. LangGraph ships with LangSmith, a full observability platform — trace every step, see every tool call, replay sessions to find exactly where something went wrong. Over 400 companies run it in production, including LinkedIn and Uber.
Limitations
- The steepest learning curve of any framework, graph-based thinking is non-trivial for teams without strong fluency in Python.
- Verbose configuration; simple tasks require disproportionate setup.
- Rapid version changes often leave documentation behind the codebase.
- Debugging complex graphs without LangSmith becomes painful at scale.
Best For
- Complex enterprise workflows needing branching logic and loops.
- Teams that require full audit trails and deep debugging capability.
- Production systems where observability is non-negotiable.
- LinkedIn, Uber-scale deployments with multi-step conditional flows.
2. Microsoft Agent Framework (formerly AutoGen)
AutoGen came from Microsoft Research with a conversational model: agents talk to each other in a group-chat structure, sending messages, delegating, and collaborating until they converge. This handles dynamic, unpredictable tasks well because agents can adapt through dialogue rather than following a rigid workflow.
Critical 2026 update: AutoGen is now in maintenance mode. Microsoft has merged AutoGen and Semantic Kernel into the Microsoft Agent Framework, with general availability targeted for Q1 2026. No new features are coming to AutoGen. If you are starting a new build, use the Agent Framework. The migration path is documented, and the single-agent interface is nearly identical.
The Agent Framework integrates natively with Azure, Microsoft 365, and Copilot Studio. It supports human-in-the-loop configurations where a human can step into the agentic workflow at any point — essential for high-stakes decisions.
Limitations
- Heavy Azure dependency teams that are not on Microsoft infrastructure face significant integration overhead.
- AutoGen is now in maintenance mode; no new features are shipping to it.
- Conversational agent models can produce verbose, slow interactions for simple tasks.
- Migration from AutoGen to Agent Framework, while documented, adds engineering cost for existing teams.
Best For
- Azure-native enterprise deployments are already in the Microsoft ecosystem.
- High-compliance environments require human oversight at decision points.
- Workflows integrated with Microsoft 365 and Copilot Studio.
- Regulated industries where human-in-the-loop is a hard requirement.
3. CrewAI
The most natural way to think about multi-agent systems is to view them as teams. You do not think in nodes and edges. You think in roles. A Research Lead. A Writer. An Editor. CrewAI lets you build agentic workflows exactly this way — define each agent with a role, a backstory, a goal, and a set of tools, the same way you brief a new hire.
The framework handles delegation, sequencing, and coordination. You focus on what the team should accomplish. The framework figures out how they work together. The abstraction is higher-level, which means faster time to a working system but less fine-grained control over exactly how orchestration happens.
The numbers validate the approach: 100,000-plus agent executions per day, 150-plus enterprise customers, adoption at 60% of Fortune 500 companies adopting agentic AI, and an $18M Series A round. Teams consistently report shipping their first production agent in two weeks with CrewAI, compared to months with LangGraph.
Limitations
- Higher-level abstraction means less fine-grained control over orchestration logic.
- Role-based model breaks down when workflows require non-linear, highly conditional routing.
- Less mature observability tooling compared to LangGraph’s LangSmith integration.
Best For
- SMBs, founders, and product teams that need a working agent in days, not months
- Content pipelines, market research, and customer-facing workflows
- Any task that maps naturally to team roles — researcher, writer, reviewer
- First agentic deployment for teams with no prior agent-building experience
4. LlamaIndex
LlamaIndex is the knowledge infrastructure layer. While other agentic AI frameworks focus on what agents do, LlamaIndex focuses on what agents know. If your agents need to work with your company’s private data — contracts, product documentation, support tickets, financial reports — LlamaIndex is the specialist that makes that data accessible and queryable.
It handles the full data pipeline: ingesting documents, chunking them, creating vector embeddings, storing them, and retrieving the right pieces when an agent needs them. This is Retrieval-Augmented Generation (RAG) in practice — agents dynamically query your company knowledge rather than relying on stale training data.
Limitation
- LlamaIndex is almost always paired with another framework.
- Common pattern: LlamaIndex for data retrieval, CrewAI for multi-agent coordination.
- RAG pipeline quality depends heavily on the chunking strategy and embedding model choices, both of which require expertise to tune.
- Retrieval quality degrades with poorly structured or inconsistent source documents.
- Adds infrastructure overhead — vector databases, embedding pipelines — that smaller teams may not be ready to maintain.
Best For
- Any workflow where agents query proprietary company data
- Document intelligence — contracts, compliance docs, support tickets, financial reports
- Enterprise knowledge management and internal expertise search
5. Auto-GPT
Auto-GPT was released on March 30, 2023, by Toran Bruce Richards, founder of Significant Gravitas Ltd. It was one of the first widely accessible applications to showcase GPT -4’s autonomous capabilities. GitHub It connects to OpenAI’s GPT model and automates projects that would otherwise require numerous human prompts — unlike a chatbot such as ChatGPT, which requires a human to drive every step. The project went viral almost immediately, becoming the top trending repository on GitHub within days of launch.
The premise was genuinely exciting: give an AI a goal in plain language, and it would break that goal into sub-tasks, use tools like web browsing and file management, and work toward completion without continuous human input. A Tom’s Hardware article described Auto-GPT as potentially “too autonomous to be useful,” noting that its lack of mechanisms to seek clarification or correction from users often led to unhelpful or divergent outcomes.
Limitations
- Notoriously prone to looping — the agent can get stuck repeating the same steps without making meaningful progress.
- Token costs escalate rapidly because the agent reasons through every step independently, generating large prompts at each iteration.
- Low reliability in production; failure modes are hard to predict and harder to recover from without manual intervention.
- Minimal built-in observability — when something goes wrong, tracing the cause through an autonomous loop is time-consuming.
- Community activity has slowed significantly as more structured frameworks absorbed its use cases.
- Not suited for enterprise or high-stakes deployments without substantial custom guardrails built on top.
Best For
- Research, experimentation, and learning how autonomous agents behave.
- Low-stakes, exploratory tasks where unpredictable behavior is acceptable.
- Developers who want to understand the foundations of agentic loops before adopting a production framework.
- Prototyping ideas quickly when failure is not costly, and the goal is discovery, not delivery.
How to choose the right Agentic AI framework for your projects?
Before you pick a framework, understand why the stakes of this decision are higher than most teams expect. Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value, or inadequate risk controls.
The decision comes down to four filters.
- Use case first
Define whether your goal is prototyping, research, or production before you open any documentation. A team building an internal assistant with a two-week deadline has fundamentally different requirements than an enterprise team running a compliance workflow across regulated data hundreds of times a day. The framework follows the problem — never the other way around. - Workflow complexity determines architecture
If your agentic workflow has branching logic, loops, or conditional paths, you need graph-based control — LangGraph. If the work maps cleanly to roles, with tasks passing between them, CrewAI gets you to production faster with less overhead. - Your stack decides more than you think
If you are on Azure and Microsoft 365, the Microsoft Agent Framework eliminates weeks of custom integration work. If agents need to query proprietary company data, LlamaIndex belongs in your stack before you evaluate anything else. Look at where your data lives before you look at framework documentation. - Security and scale are requirements, not afterthoughts
Gartner’s August 2025 Innovation Insight report flags prompt injection and data exposure as risks that require manual safeguards, regardless of the chosen agentic AI framework. On scale: only 2% of organizations have deployed agentic AI at scale, while 61% remain stuck in exploration — and the gap almost always traces back to a framework chosen for prototyping speed rather than production resilience. Build for the scale you expect in twelve months, not the scale you have today.
What Research Says About Agentic AI Systems Today (Current state)
The agentic AI market stood at $5.25 billion in 2024, growing at a compound annual growth rate of 43.84%, with projections reaching $199 billion by 2034. 43% of companies are allocating more than half of their AI budgets to agentic systems, and 15% of day-to-day business operations will be performed by AI agents by 2028.
Then you look at who is actually running these things in production, and the story gets more grounded. Deloitte’s 2025 Emerging Technology Trends study found that while 30% of organizations are exploring agentic options and 38% are piloting solutions, only 14% have solutions ready to deploy — and a mere 11% are actively using agentic systems in production. Meanwhile, 35% of organizations have no formal agentic strategy at all.
How AI Agents Work Together in an Agentic AI System ( AI Agent vs Agentic AI )
Think about how a business operates. You could hire one generalist employee and ask them to handle your entire marketing function — strategy, research, writing, design, analytics, and distribution. For small tasks, this works. But as the work gets more complex, one person hits their limits. They can’t research and write simultaneously. They can’t review their own work with fresh eyes. The quality ceiling is low.
So instead, you build a team. A strategist who defines what you’re trying to achieve. A researcher who digs up the insights. A writer who turns insights into content. An editor who ensures quality. A publisher who handles distribution. Each specialist is better at their job than the generalist was. And together, they produce higher-quality work faster than any single person could.
An AI agent is a generalist employee. An agentic AI system is the equivalent of a fully staffed marketing department. The framework you choose is the operating system that manages how the team communicates, delegates, and delivers.
The practical implication: when someone asks you whether to build an “AI agent” or an “agentic AI system,” the right question in response is: “How complex and multifaceted is the workflow?”
A customer support chatbot that handles refund requests? That’s more likely a task for a single agent. An autonomous system that researches customer pain points, drafts targeted marketing copy, A/B tests it, analyzes performance, and iterates? That’s a call for an agentic AI system.
Can You Build an AI Agent Today? What’s Next in This Series
Every major technology shift has a window. A moment where the people who paid attention, asked the right questions, and moved thoughtfully came out ahead, not because they were the biggest or the fastest, but because they understood what they were working with before everyone else did.
Agentic AI is in that window right now.
So if something in this article sparked an idea — a workflow that feels broken, a problem that keeps coming back, a process you have always wished someone else could handle — that is worth exploring. Bring it to us. Tell us what your business looks like, where the friction is, and what you have already tried. We will think through it with you.
In the next episode of this series, we’ll go hands-on. We’ll walk you step by step through building your first AI agent from scratch — choosing a framework, setting up your environment, giving your agent tools, and running it on a real task. If you have a use case in mind for your business, comment below, email us, or book a quick call.
See you there!
