Five Intelligent Agent Patterns

1. Introduction

With the rapid development of AI-driven applications, developers increasingly utilize large language models (LLMs) to build intelligent agents for efficiently executing complex tasks. However, the most effective implementation does not rely on complex frameworks but rather on simple and composable design patterns.

This article explores the difference between workflows and intelligent agents and clarifies key patterns commonly found in AI-driven systems.

2. What is an AI Intelligent Agent?

An AI intelligent agent is an autonomous system that uses LLMs to process information, interact with tools, and execute complex tasks. As we move through 2026, the definition has evolved from simple “completion bots” to “action-oriented entities.” These agents are classified into three major levels:

  • Workflows: LLMs interact with external tools in a structured sequence along a predefined execution path.
  • Autonomous Agents: Dynamic systems where LLMs independently decide on processes, select tools, and determine how tasks are completed.
  • Agentic Ecosystems (Multi-Agent Systems): Collaborative swarms of specialized agents working together to solve multi-domain problems.

The choice depends on the problem domain: workflows excel in structured automation, while multi-agent swarms are now the gold standard for enterprise-scale dynamic decision-making.

3. Key Patterns in AI Intelligent Agent Systems

3.1 Chain Workflow Pattern

The chain workflow organizes multiple steps in a linear sequence, where the output of one step serves as the input for the next. This provides clear control while allowing a certain degree of adaptability. It is suitable for tasks with well-defined sequential steps, where each step depends on the previous one.

The chain workflow improves accuracy by linking prompts or tasks in a structured manner. Each task’s output feeds into the next, forming a continuous processing chain.

Example:

In a news recommendation system, the workflow may first retrieve user preferences, then use these preferences as input to fetch and analyze news. This is a typical chained task where the output of user preferences directly serves as the input for news retrieval.

Suitable Scenarios:

  • Tasks with a clear sequence of steps.
  • Need for higher accuracy at the cost of processing time.
  • Each step depends on the output of the previous step.

3.2 Parallelization Workflow Pattern

This pattern improves efficiency by executing multiple tasks simultaneously, making it suitable for data-intensive operations requiring large-scale parallel processing. It is particularly useful in applications needing fast responses, such as big data analysis, real-time monitoring, and complex decision-support systems.

Parallelization workflows involve performing multiple tasks or processing multiple datasets at the same time to improve speed and efficiency. Tasks that can be independently executed in parallel make full use of system resources, reducing overall processing time.

Example:

In a financial analysis project, stock market, forex market, and commodity market data might need simultaneous analysis. By assigning these tasks to separate LLM calls (e.g., LLM Call 1, LLM Call 2, and LLM Call 3), each call independently processes its assigned market data. A central aggregator then collects and integrates these results into a comprehensive report.

Suitable Scenarios:

  • Handling large amounts of similar but independent tasks.
  • Tasks requiring multiple independent perspectives.
  • Tasks that can be parallelized and require fast processing times.

3.3 Routing Workflow Pattern

The routing workflow dynamically directs execution paths based on input conditions, enabling the system to adapt to different scenarios without predefined sequences.

Routing workflows intelligently route tasks to specialized processes based on input characteristics or conditions. This allows the system to select different processing paths dynamically.

Example:

A financial services platform may route user requests to different API endpoints based on their interest topics (e.g., “crypto” or “stocks”). This is an example of a routing workflow where the input topic determines the request’s processing path.

Suitable Scenarios:

  • Tasks with complex input categories.
  • Different inputs require specialized handling.
  • Input can be accurately classified.

3.4 Orchestrator-Worker Pattern

An orchestrator AI delegates tasks to multiple specialized worker agents, each responsible for different functions (e.g., data retrieval, analysis, summarization).

In this pattern, a central AI (the orchestrator) assigns tasks to specialized subprocesses (workers). This enables the system to break down complex tasks into multiple subtasks and assign them to different workers for parallel processing.

Example:

In a news analysis project, a service can act as the orchestrator, coordinating AI models for news retrieval and analysis. AI models (e.g., OpenAI’s ChatModel) serve as specialized text analysis workers, handling specific analytical tasks.

Suitable Scenarios:

  • Tasks are complex and unpredictable in terms of subtasks.
  • Tasks require different approaches or perspectives.
  • Problems require adaptive solutions.

3.5 Evaluator-Optimizer Pattern

An evaluator assesses the quality of an agent’s output, while an optimizer refines responses based on feedback. This pattern forms the basis of Self-Correction systems that minimize hallucinations.

Suitable Scenarios:

  • High-stakes tasks requiring 99%+ accuracy.
  • Iterative code generation or technical writing.

3.6 Multi-Agent Collaboration (Swarm Intelligence)

In 2025-2026, the focus shifted from single “god-models” to Multi-Agent Systems (MAS). Instead of one agent doing everything, a “Swarm” of specialized agents (Legal Agent, Financial Agent, DevOps Agent) collaborates via shared memory.

Example:

An enterprise software deployment where a “Planner Agent” breaks down the task, a “Coder Agent” writes the logic, and a “Security Agent” audits the code in parallel before a “Deployer Agent” executes the release.

3.7 The Standard Shift: Model Context Protocol (MCP)

A major milestone in 2026 is the widespread adoption of the Model Context Protocol (MCP). This universal standard allows agents to connect to any data source (Google Drive, Slack, GitHub) without writing custom integrations for every model. It has replaced “Prompt Engineering” with “Context Engineering,” focusing on how to provide the most relevant data to the agent at the right time.

4. Comparison of Modern AI Agent Patterns (2026)

The following comparison table provides a more comprehensive view of each pattern’s characteristics, advantages, challenges, and applicable scenarios, helping in selecting the most suitable approach when designing AI agent systems.

Comparison Item Chain Workflow Pattern Orchestrator-Worker Evaluator-Optimizer Multi-Agent Swarm Parallelization
Definition Sequential processing chain. Central AI delegates to sub-agents. Iterative evaluation and refinement. Collaborative network of specialists. Simultaneous task execution.
Primary Goal Predictable automation. Task decomposition & scale. Output quality & precision. Cross-domain problem solving. Throughput & latency reduction.
New in 2026 Integrated via MCP for data. Dynamic worker spawning. Deep-reasoning reflection. Swarm Intelligence & Memory. Massive-scale AIOps.
Best Scenario Linear, low-complexity tasks. Complex but bounded projects. Iterative drafts (Code/PRs). Unstructured enterprise goals. Data ingestion & Monitoring.
Governance Simple tracing. Centralized auditing. HITL (Human-in-the-Loop). Distributed ethical audits. Automated anomaly detection.