This Practitioner's Guide Covers Six Advanced Architectural Patterns for Agentic Systems.

Meta description: A practical guide to six agentic AI architecture patterns, from the ReAct loop to adaptive routing, and why context engineering decides production success.

Tags: agentic AI, AI agents, context engineering, multi-agent workflows, LLM architecture

![Abstract illustration of connected nodes and flowing pathways representing AI agent architecture]

If you are building AI agents and wondering why your proof-of-concept works beautifully in a demo but falls apart under real traffic, the problem is usually not the model. It is the architecture around it. This guide walks through six distinct patterns for building agentic systems, explains the context engineering techniques that keep them affordable, and tells you when each pattern belongs in production.

Why agents exist alongside conversational AI

Conversational AI and agentic AI have become two separate foundational uses of generative AI. The difference matters: conversational insight pulled from data is often not enough on its own. Large language models can plan, reason, and act, and agents are the logical constructs wrapped around that LLM "brain." They use tools, talk to external APIs, correct their own mistakes, and collaborate with other agents.

That collaboration depends on many high-frequency decisions: what the next step should be, which agent handles it, which tool to call, and whether there is enough context to answer the user. A significant shift here is the arrival of low-cost calibrated decision models, following System One-style approaches such as TypeSafe's JEV. These move deterministic workload off the LLM, leaving the big model free to focus purely on deep reasoning and synthesis.

The industry is also moving away from naive single-agent loops toward heavily engineered multi-agent workflows and constrained architectures. Getting an agent from proof-of-concept to a reliable, production-ready enterprise workflow means qualifying it on scalability, responsiveness, and cost-effectiveness.

The four pillars of any agent

Before comparing architectures, it helps to define what every agent is made of.

The core LLM (reasoning engine). This is the foundational model that orchestrates logic, such as GPT-5, Claude 5.5 Sonnet, or Llama-4. In agentic systems, native support for tool calling (also called function calling) is crucial.

Tools and actions. These are executable functions the agent can call, ranging from API wrappers like `search_web`, `query_database`, and `send_email` to code interpreters that run Python scripts in a sandbox.

Planning and control flow. This is how the agent decides what to do next. It can be rigid, like a state machine or directed acyclic graph, or fluid, like an open-ended reasoning loop.

Memory. This is the system's ability to retain information across steps and interactions, covering both short-term working memory (the active prompt window) and long-term episodic memory (vector stores of past interactions).

Context engineering: the hidden success criterion

In agentic systems, context engineering is a key criterion for production readiness. Traditional prompts have static context. An agent's context is highly dynamic. As it acts, it accumulates observations, errors, and intermediate thoughts. Append every observation to the prompt and the context window bloats, driving up latency and cost. Worse, the LLM may lose track of granular facts buried in the middle of a very large block of text, producing incorrect or incomplete responses downstream.

Several techniques keep this under control:

State projection. Inject only the context strictly necessary for the current step. If an agent has moved from research to writing, the raw research logs should leave the active context.

Memory pruning and summarization. Use secondary LLM calls, often smaller and cheaper models like GPT-mini, to compress past steps before appending them. A typical instruction: summarize the last 5 search results into key facts.

Structured scratchpads. Prompt the LLM to write intermediate reasoning into designated JSON fields or XML tags, such as `<thought> I should check the API docs </thought>`. These can be parsed and filtered out in later turns to save space.

Semantic memory retrieval. Treat past agent actions as a vector database. Rather than loading the entire conversation history, the agent retrieves only past actions semantically relevant to the current obstacle.

Prompt caching. Offered by providers such as Anthropic and OpenAI. In complex agentic systems, the system prompt and tool definitions can easily exceed 5,000 tokens, yet they are static instructions that never change during the system's lifecycle. By placing static system instructions at the top of the context window, providers cache the precomputed attention states into a Key-Value (KV) cache. When the agent loops 10 times, you only pay for newly appended observations, which can drastically cut inference costs.

Human-in-the-Loop as a control mechanism

Enterprise applications cannot trust autonomous agents to execute without supervision. LLMs are inherently non-deterministic, so anything that is not a read operation could be undesirable or harmful. Dropping a SQL table, authorizing a payment, or emailing a client all fall into this category. Human-in-the-Loop (HITL) is a control flow mechanism where the agent pauses its execution state and waits for a human reviewer.

In a state graph implementation such as LangGraph, invoking a specific tool like `send_email_tool` interrupts the graph. The state is serialized to a database and execution halts. A human reviews the drafted email in a UI, edits it if needed, and clicks approve. The orchestrator then resumes graph execution with the approved state.

Pattern 1: The standard ReAct loop

The ReAct (Reason plus Act) pattern is the foundational building block of agentic workflows. The LLM alternates between thinking about what to do, acting by calling tools and observing results, then thinking again about the result and its relevance to the goal.

The agent receives a system prompt describing its persona, available tools, and rules. When a user provides a task, the agent enters a while loop. Each iteration outputs a thought explaining its logic, followed by an action, which is a tool call. The orchestration layer intercepts the action, executes the tool, and returns the observation to the LLM. The LLM reassesses. This continues until it decides it has enough information for a final response.

A useful context engineering move here is managing the agent scratchpad. As the loop progresses, the prompt grows linearly: system prompt, plus user query, plus thought 1, action 1, and observation 1, and so on. To prevent bloat, implement observation truncation. If a tool returns a large HTML document, the orchestration layer should truncate it or run a summarization script before feeding it back. Otherwise a single web search can overwhelm the context window.

The strengths are flexibility and error recovery. ReAct handles unpredictable tasks because the solution path is not hardcoded, and when a tool fails with something like a 404 error, the error feeds back to the LLM, which can reason around it by trying a different URL, correcting parameters, or falling back to another tool.

The weaknesses are latency and cost. A single query might require 10 sequential LLM calls. Even with prompt caching, that takes time and consumes significant tokens. For highly complex tasks, the agent may forget its original goal, entering a chain of irrelevant tool calls or looping endlessly on a broken tool.

ReAct suits open-ended, research-oriented tasks where the exact sequence of steps cannot be known in advance: data analysis, deep-dive web research, or debugging code in an isolated sandbox. It is generally not recommended for deterministic, low-latency, user-facing chat applications where people expect precise replies quickly.

Pattern 2: The sequential multi-agent workflow

When a task is too complex for a single ReAct agent to handle reliably, break it down. The sequential multi-agent workflow is a factory assembly line. Instead of one super agent doing everything at once, specialized agents are chained, passing the output of one as structured input to the next.

Each agent in the sequence has a focused persona, a limited set of tools, and a very specific context window. A typical three-stage chain looks like this. The researcher gathers raw data using search tools and scrapes web pages, compiling a raw dossier. The analyst receives the dossier, processes the data, performs numerical calculations often by writing Python code, and extracts key metrics. The writer takes those metrics and formats them into a final, client-ready report.

This architecture demonstrates context engineering through state projection. The writer agent does not need to see the 50 web pages the researcher scraped, nor the four attempts it took the analyst to write a working Python script. It only receives the finalized analytical brief. Isolating context windows this way prevents the "lost in the middle" problem and significantly reduces token usage. Implementation typically uses state graphs like LangGraph, passing a strict state object between nodes.

The advantages are reliability, token efficiency, and debuggability. Specialized prompts yield significantly higher quality results than generic do-everything prompts, and you can tune temperature and model sizes individually, using a cheap model for researching and an expensive one for analysis. Because each step is isolated, you can determine exactly which agent failed and checkpoint state after each node to resume later.

The limitation is strict linearity. It is difficult to handle the case where the writer realizes the researcher missed something critical and the workflow needs to go backward. Cycles can be added to state graphs, but they complicate the architecture and risk infinite loops.

Use sequential workflows for well-defined, multi-stage pipelines: content generation, data processing, ETL tasks, or anything that mimics a traditional human assembly line such as drafting, editing, formatting, and publishing.

Pattern 3: The adaptive router agent

Not every query can be answered well by a linear workflow. The adaptive router treats the agentic system as an intelligent triage system rather than an open-ended problem solver. It analyzes user intent and routes execution to a sub-pipeline specialized for that intent.

The router agent is typically a fast, calibrated LLM such as GPT-mini or Claude Haiku, or a non-autoregressive JEV model. It receives a user query and a list of available pipelines, for example `technical_support`, `billing_inquiry`, and `general_escalation`. Its only job is intent classification and entity extraction, outputting a structured JSON decision. Once that decision is made, the router's job is done and traditional deterministic code takes over.

A query can also carry mixed intent, both technical and billing. In that case, multiple pipelines execute in parallel and their outputs are combined in a synthesis step.