A deterministic chain follows a rigid route:<\/p>\n\n\n\n
While deterministic chains handle basic tasks like text transformation or single-pass summarization, they struggle with real-world business logic. If a database query returns an unexpected format, or if a third-party API returns a rate-limit error, a linear chain breaks.<\/p>\n\n\n\n To handle dynamic tasks, Xicom’s engineering experts build AI agents that rely on a language model as a decision-making engine. Given a goal, the model evaluates incoming data, selects external tools, inspects execution outputs, and loops dynamically until it completes the assigned task. Therefore, by establishing proper memory structures, strict tool schemas, and persistent state controls, we ensure your software remains scalable, secure, and closely aligned with business goals. Before directly moving towards the steps of building AI agents with LangChain, let us first understand the terminology.<\/p>\n\n\n\n LangChain is an open-source framework designed to simplify the construction of applications powered by large language models (LLMs)<\/a>. A language model, on its own, just takes text in and produces text out. It has no memory of a previous exchange, no way to look something up in a database, and no way beyond generating a response. LangChain exists to close that gap.<\/p>\n\n\n\n It gives developers a set of standard interfaces for connecting a model to documents, APIs, databases, other models, and a defined sequence of steps. Instead of writing custom glue code every time you want a model to pull data from a file before answering, or call an external tool mid-conversation, LangChain provides the abstractions for that. It provides it in a form that works consistently across model providers like OpenAI, Anthropic, or open source models running locally.<\/p>\n\n\n\n The open-source framework is constructed around a fairly simple idea. Most applications don’t always need a single call to a model, they need a sequence of calls, decisions, and data lookups strung together. LangChain calls a fixed sequence like this a chain. A chain might take a user’s question, retrieve relevant documents, insert them into a prompt, and pass that to the model, all as one defined pipeline. LangChain’s expression language (LCEL) does allow some conditional branching inside a chain, but the possible paths are still fixed by the developer ahead of time; the model itself isn’t deciding the route at runtime. That distinction is what separates a chain from an agent, covered below.<\/p>\n\n\n\n Let’s discuss the components of LangChain in detail. The LangChain framework is structured into several core, modular components. Each component below handles one task, and you combine only the ones your application actually needs.<\/p>\n\n\n\n Model Inputs\/Outputs:<\/strong> The interface layer that maintains interactions across different LLM providers, such as OpenAI, Anthropic, or Hugging Face, or self-hosted open-source model families like Meta’s Llama. It manages Prompt Templates to structure dynamic inputs, Language Models to process those inputs, and Output Parsers to extract structured formats, like JSON, Pydantic objects, or SQL queries, from raw model responses.<\/p>\n\n\n\n Data Connection (Indexing & Retrieval)<\/strong>: It is a suite of utilities built to interface LLMs with enterprise data. This component includes Document Loaders to ingest unstructured data from hundreds of sources, such as PDFs, Slack, databases, S3, Text Splitters to break large documents into semantically meaningful chunks, Vector Stores to manage high-dimensional embeddings, and Retrievers to query and fetch relevant context during inference.<\/p>\n\n\n\n Chains<\/strong>: A chain links multiple steps together into a single callable pipeline. A chain links prompts, models, and data transformers together. This allows developers to move beyond single-prompt interactions and create multi-step processing workflows where the output of one step becomes the input for the next.<\/p>\n\n\n\n Memory<\/strong>: The persistent storage mechanism that addresses the stateless nature of base LLM calls. Memory components store, summarize, and manage interaction histories\u2014allowing applications to maintain context across multi-turn user conversations without overwhelming model token limits.<\/p>\n\n\n\n Agents & Tools<\/strong>: The dynamic orchestration engine of the framework. Unlike deterministic chains that follow a fixed path, Agents use an LLM as a reasoning driver to decide which actions to take and in what order. Tools are the functional capabilities granted to the agent, such as web search APIs, code execution sandboxes, or database connectors. The agent evaluates the current state, selects a tool, parses its output, and loops until it completes the goal. In current LangChain projects, this looping behavior is most often built with LangGraph (covered next) rather than the older standalone agent executor pattern.<\/p>\n\n\n\n Callbacks<\/strong>: Lastly, it is an observability and logging integration layer. Callbacks hook into every stage of the execution lifecycle, allowing developers to trace intermediate steps, track token consumption, measure latency, and debug complex multi-step chains in production environments.<\/p>\n\n\n\n Also Read: How to Build an AI Agent<\/a><\/em><\/strong><\/p>\n\n\n\n LangGraph is an open-source library built by the LangChain team to extend the capabilities of language model applications from simple, linear pipelines into stateful, multi-actor, and cyclic graph structures. Standard chains process data in a straight line. An input flows through a prompt, hits an LLM, triggers a tool, and returns a response.<\/p>\n\n\n\n LangGraph processes the data differently. It does this by constructing application workflows as distinct state machines. In a LangGraph architecture, every step in a process is represented as a node, while the transitions between those steps are known as edges. Nodes read from and write to a shared, persistent state object that maintains context across the entire lifecycle of a task.<\/p>\n\n\n\n Cyclic Workflows<\/strong>: Unlike traditional Directed Acyclic Graphs (DAGs) where data can only move forward, LangGraph supports explicit execution loops. A node can execute repeatedly, such as an agent refining code or retrying a failed API call until specific pass\/fail conditions are met.<\/p>\n\n\n\n Explicit State Management<\/strong>: All nodes interact with a centralized state schema (often defined using standard Python dictionaries or TypedDict models). Every state modification is transparent, predictable, and fully traceable, making debugging and auditing straightforward.<\/p>\n\n\n\n Built-in Persistence & Checkpointing<\/strong>: LangGraph automatically saves application state at every step using configurable checkpointers. If a server restarts, an external API drops out, or a process times out, execution can resume from the exact node where it stopped without re-running the entire workflow. (The step-by-step guide below includes a working checkpointer example.)<\/p>\n\n\n\n Human-in-the-Loop Interruption<\/strong>: Enterprise operations such as sending customer emails, approving refunds, or updating production databases often require human validation. LangGraph allows developers to set interrupt gates before specific nodes, pausing execution until a human operator reviews the current state, approves the action, or modifies the data before resuming.<\/p>\n\n\n\n Multi-Agent Coordination<\/strong>: Complex workflows can be broken down into specialized agents operating as nodes within a primary graph or as isolated sub-graphs. A supervisor graph can delegate work to sub-agents (such as a database query specialist and a compliance checking specialist) and aggregate their outputs back into a unified state.<\/p>\n\n\n\n[Input Query] ---> [Prompt Template] ---> [LLM Call] ---> [Output Parser] ---> [Result]<\/code><\/p>\n\n\n\n![\"How-to-Build-AI-Agents-with-LangChain\"](\"https:\/\/www.xicom.biz\/blog\/wp-content\/uploads\/2026\/09\/How-to-Build-AI-Agents-with-LangChain-1024x683.webp\")
<\/a><\/figure>\n\n\n\n<\/span>What Is LangChain?<\/span><\/h2>\n\n\n\n
Components of LangChain<\/h3>\n\n\n\n
<\/span>What is LangGraph?<\/span><\/h2>\n\n\n\n
Key Technical Capabilities of LangGraph<\/h3>\n\n\n\n
<\/span>Understanding the Architecture: How Agentic AI Systems Work?<\/span><\/h2>\n\n\n\n