Agentic AI represents a paradigm shift in artificial intelligence, moving beyond static responses to dynamic, goal-oriented systems capable of autonomous action. At its core, Agentic AI leverages language models as reasoning engines that coordinate tools, data sources, and even other AI agents to solve complex problems.
This evolution is powered by frameworks like LangChain (available in both Python and TypeScript) and specialized tools like LangSmith, LangGraph, and Pydantic-AI that enable sophisticated multi-agent architectures.
From chains to autonomous agents
The evolution of AI from static workflows to dynamic, goal-oriented systems hinges on two core concepts: chains and agents. These building blocks form the backbone of frameworks like LangChain and Pydantic-AI, enabling developers to create increasingly sophisticated AI applications. Let’s explore how these components work, their origins, and their role in modern AI systems.
Chains: structured workflows for predictable tasks
The term chain originates from software engineering, where it describes a sequence of operations executed in a defined order. In AI, chains are predefined workflows that combine language model calls, API requests, or data transformations. For example, a ConversationalRetrievalQAChain might:
-
Retrieve relevant documents
-
Format them into a prompt
-
Query an LLM
-
Parse the response
Chains excel at tasks requiring reproducibility, such as data preprocessing pipelines or templated customer service responses. They provide stability but lack adaptability, a limitation addressed by agents.
Agents: dynamic decision-makers
Derived from intelligent agent theory in AI research, agents are autonomous systems that use LLMs to decide action sequences in real-time. Unlike rigid chains, agents:
-
Evaluate the context to choose tools
-
Modify behavior based on intermediate results
-
Handle open-ended scenarios
Agents act as intelligent orchestrators that use tools to interact with the world, gather information, and solve problems. Tools can be anything from a web search API, a database query function, a mathematical calculator, or even another agent equipped with its own specialized abilities.
When an agent receives a user query, it first reasons about which tools are needed to fulfill the request. For example, if asked, "What was the weather in Paris on the day the Eiffel Tower opened, and how does it compare to today's weather?" an agent might use a search engine tool to find the opening date, a historical weather API to retrieve past conditions, and a current weather tool to fetch today's data.
The agent can call these tools in sequence, analyze the results, and synthesize a final answer for the user. This process is dynamic: the agent can loop through tool calls as many times as needed, feeding the results of one tool into another, and even deciding mid-process to switch strategies if new information emerges.
One often overlooked—but critical—aspect of this process is the environment where agents execute code or perform actions. When LLMs generate and run code dynamically, ensuring a secure and ephemeral execution layer is essential to prevent risks like unintended side effects or security breaches. Tools like Daytona provide sandboxed, stateful runtimes where agents can safely execute code, run isolated experiments, or interact with files and APIs — all without compromising the integrity of the host system. This kind of execution layer becomes especially important in production-grade multi-agent systems where traceability, fault tolerance, and resource control are non-negotiable.
Agentic AI frameworks: a fast-track overview
There are developer-friendly frameworks that let you kick off agentic AI projects in minutes: LangChain offers modular SDKs for chaining LLMs with tools and data sources, while LangGraph introduces a graph-based orchestration layer for complex multi-agent pipelines.
LangSmith provides full-stack observability and evaluation to monitor agent runs and optimize performance, PydanticAI leverages Pydantic’s schema validation to ensure type-safe, production-grade agent interactions, and Autogen (AG2) lets you rapidly assemble cooperative agent teams with built-in handoff primitives.
LangChain
LangChain is an open-source framework designed to simplify the development of applications powered by large language models (LLMs), such as chatbots, virtual agents, and intelligent search systems. Its core components are chains and agents.
Available for both Python and JavaScript/TypeScript, LangChain provides a modular architecture that allows developers to build context-aware reasoning applications by connecting LLMs with external data sources, APIs, and custom workflows.
LangGraph: orchestrating complex, multi-agent workflows
LangGraph extends the capabilities of LangChain by introducing a graph-based orchestration framework for agentic workflows. Unlike traditional chains or single-agent systems, LangGraph allows developers to design workflows as directed graphs, where each node represents an agent or a specialized function, and edges define the flow of information and control.
This architecture enables the creation of sophisticated multi-agent systems, where agents can collaborate, branch, and cycle through tasks, sharing state and memory as needed. For example, a LangGraph workflow might include a research agent, a data analysis agent, and a report-generation agent, each operating independently but coordinating through the graph’s structure.
LangGraph’s built-in state management, persistence, and support for human-in-the-loop interventions make it possible to build robust, fault-tolerant applications that can recover from errors and adapt to complex, real-world scenarios.
This modular, graph-based approach is particularly powerful for enterprise use cases that require flexibility, specialization, and collaboration among multiple autonomous agents.
LangSmith: observability and evaluation for agentic systems
LangSmith complements LangChain and LangGraph by providing a comprehensive observability and evaluation platform for agentic AI applications. As agents and multi-agent systems grow in complexity, monitoring their behavior, debugging failures, and ensuring high-quality outputs become critical challenges.
LangSmith addresses these needs by offering real-time tracking of agent and chain executions, detailed step-by-step breakdowns of LLM interactions, and dashboards for tracking key business metrics like latency, cost, and response quality. Developers can use LangSmith to evaluate agent performance through automated and human-in-the-loop assessments, iterate quickly on prompt and model changes, and set up alerts for production issues such as increased error rates or degraded feedback scores.
By integrating deeply with both LangChain and LangGraph, LangSmith empowers teams to build, deploy, and maintain reliable agentic AI systems at scale, ensuring transparency, efficiency, and continuous improvement throughout the development lifecycle.
Pydantic-AI
Pydantic-AI is a Python library that stands out for its strong emphasis on explicit schema validation and structured data handling, leveraging Pydantic models to ensure that both the inputs and outputs of AI agents conform to well-defined types and constraints.
Unlike LangChain, which offers a flexible, component-based approach with a vast ecosystem of built-in integrations, chains, and memory modules for rapid prototyping, Pydantic-AI prioritizes robustness and predictability by requiring developers to define clear data models for every agent interaction. This means that while LangChain is ideal for quickly assembling complex workflows and integrating with a wide range of tools and databases, it often relies on more implicit data contracts-whereas Pydantic-AI enforces strict type safety and validation at every step, reducing the risk of unpredictable outputs and making error handling more systematic.
However, Pydantic-AI is newer and has fewer out-of-the-box integrations, so developers may need to write more custom code to connect to databases or external systems, trading some convenience for greater control and reliability. Ultimately, Pydantic-AI is best suited for production-grade applications where structured, validated outputs are critical, while LangChain excels in rapid development and experimentation with a rich ecosystem and broader language support.
AG2 (AutoGen)
AG2 (formerly AutoGen) is an open-source “AgentOS” framework designed to streamline the creation and coordination of AI agents through a conversational API centered on the ConversableAgent class.
It provides higher-level abstractions, such as GroupChat for multi-agent dialogue patterns and Swarm for orchestrating collective decision-making, along with built-in support for retrieval-augmented generation, code execution, and integration with external tools. Rebranded from AutoGen 0.2.34 in late 2024, AG2 remains community-driven via an open RFC process and supports multiple LLM providers, including OpenAI, Anthropic, and Google’s Gemini series.
Both LangGraph and AG2 enable seamless assembly of multiple LLM-driven agents and external tools into cohesive workflows, yet they differ in architecture and developer experience. LangGraph models your pipeline as a directed graph: each node is an agent or specialized function, and edges explicitly govern control and data flow, offering built-in state persistence, fault tolerance, and branching logic for long-running, enterprise-grade processes.
AG2 takes a conversational-first approach: its core ConversableAgent class standardizes message exchange between agents, while abstractions like GroupChat and Swarm automate multi-agent collaboration with minimal boilerplate, making rapid prototyping of agent teams and human-in-the-loop scenarios straightforward.
Choose LangGraph when you require fine-grained, graph-centric orchestration, persistent state management, and support for complex branching workflows; opt for AG2 for a chat-like, developer-friendly API that accelerates building and experimenting with cooperative agent networks.
Multi-agent systems
Multi-agent systems span a spectrum of complexity. At the simplest end, an AI agent invokes a tool that is itself another agent, forming a minimal multi-agent system. More elaborate setups layer agents in hierarchies or peer networks, and interaction paradigms range from fully cooperative to adversarial.
Frameworks like OpenAI Agents SDK enable basic handoffs, while platforms such as LangGraph or Autogen (AG2) allow you to graph out intricate workflows. Surveys of multi-agent learning characterize how these designs trade off scalability, fault tolerance, and autonomy across domains from robotics to enterprise RAG systems.
Agent-as-tool: simple multi-agent systems
A basic multi-agent system arises when one agent treats another as a “tool” to extend its capabilities. In practice, the primary agent reasons about which sub-agent to call and delegates specific tasks, much like calling an API, then integrates the sub-agent’s output into its own reasoning.
The OpenAI Agents SDK formalizes this pattern by letting an LLM-driven “Orchestrator Agent” use handoffs to sub-agents equipped with distinct tools (e.g., web search, calculator, summarizer). The orchestrator can loop through tool calls and sub-agent invocations until the goal is met.
Hierarchical multi-agent systems
Hierarchical MAS arranges agents in layers: high-level agents set strategy and delegate sub-tasks to lower-level agents, who may themselves spawn further sub-agents. This chain of command boosts scalability and fault tolerance by isolating failures at lower layers.
In holonic MAS, each “holon” behaves both as an autonomous agent and as a component of a larger agent, echoing biological systems like organs within a body. A lead agent appears as a single entity externally but comprises multiple subagents internally.
Using LangGraph, you can model a research workflow as a directed graph: a “Manager Agent” node assigns tasks to “Web Searcher,” “Data Analyst,” and “Report Generator” agents. LangGraph’s state persistence and branching support real-time monitoring and recovery in production settings.
Networked & cooperative/competitive multi-agent systems
Network vs. supervisor architectures
-
Networked MAS: Agents communicate peer-to-peer to decide dynamically which agent should act next, promoting decentralization and resilience against single points of failure.
-
Supervisor MAS: A central “Supervisor Agent” orchestrates peers by selecting the next actor, trading off some resilience for simpler global control.
Cooperative vs. competitive interaction
Agents may collaborate toward shared objectives or compete for resources and rewards. Cooperative MAS excel in joint tasks like coordinated search, while competitive MAS use game-theoretic strategies to optimize individual performance. Many real-world systems blend both modes, with agents cooperating under one regime and competing under another.
Embodied & virtual multi-agent surveys
Embodied Multi-Agent Systems (EMAS) integrate virtual agents with physical robots or simulators, enabling tasks in logistics or warehouse automation. Recent surveys highlight how generative foundation models enrich agent communication, paving the way for adaptive, collaborative behaviors in both physical and digital environments.
Conclusion
Agentic AI represents the transition from reactive, prompt-driven models to proactive systems that autonomously plan, reason, and act in complex environments. By abstracting core patterns—chains, agents, graph orchestration, observability, and schema validation—frameworks like LangChain, LangGraph, LangSmith, PydanticAI, and AG2 dramatically lower the barrier to entry for building sophisticated multi-agent applications.
Enterprises are already leveraging these tools to automate customer support, knowledge retrieval, and decision workflows, marking a fundamental shift in AI strategy and integration across business functions. Best practices are emerging around observability, error handling, and human-in-the-loop evaluations to ensure reliability and continuous improvement.
At the same time, concerns around data privacy and governance, especially when agents coordinate across sensitive systems, underscore the need for robust security and ethical frameworks. Moving forward, practitioners should adopt a test-and-learn approach, iterating on agent designs while adhering to schema validation, monitoring key metrics, and engaging stakeholder feedback.
As these frameworks evolve—becoming more user-friendly, extensible, and secure—they will enable the next generation of AI-driven products, from autonomous RAG systems to collaborative robot swarms, transforming how organizations innovate and operate.
