Five key design patterns for Agentic AI

Agentic AI is revolutionizing the way we design AI systems, moving from the traditional linear query-response model to autonomous adaptive agents that can reason, plan, and collaborate. Below we take a deep dive into the five key design patterns of Agentic AI: Reflection, Tool Invocation, ReAct, Planning, and Multi-Agent. Each pattern enhances the autonomy of AI systems through structured workflows.

1. Reflective Mode: The Art of Self-Correction

Reflective mode enables AI to act as its own critic, continuously iterating its output to improve accuracy and quality. AI using this mode generates an initial response, evaluates it for errors or areas for improvement, and improves it through multiple iterations — a process similar to how humans revise their work.

How it works

• Users submit queries to an LLM (e.g., DeepSeek LLM).

• LLM generates initial output.

• The LLM (or the same model in reflective mode) reviews the output, identifies deficiencies and makes recommendations for improvements.

• This process is repeated until the output reaches a predefined quality threshold.

Scenario

In automated content creation, Reflection ensures factual accuracy and consistency. For example, a scientific paper written by AI may initially contain outdated data. With Reflection, it can cross-check references, correct errors, and ultimately produce a polished and reliable article.

Latest Trends

A study from Deeplearning.ai highlights that reflection-based workflows have become mainstream for high-stakes applications such as legal document generation, where accuracy is critical. Models such as xAI’s Grok 3 and Anthropic’s Claude 3.7 have  integrated Reflection  as a core feature, improving output quality by up to 35% compared to single-shot generation. In addition, techniques such as “self-consistent hints” are being used to enhance reflection, where the AI ​​generates multiple outputs and selects the most consistent one, minimizing hallucinations.

challenge

Reflection mode can be very resource-intensive, requiring multiple calls to the LLM, which increases latency and computational cost. As the DeepSeek LLM’s GitHub page states, biases in the training data can persist across iterations, potentially amplifying errors. To mitigate this, companies like xAI are experimenting with lightweight “bias auditors” — small models that flag biased outputs during reflection cycles, reducing bias propagation.

2. Tool usage model: expanding the vision of artificial intelligence

The tool usage model equips the AI ​​with external tools to overcome the limitations of its internal knowledge. For example, the LLM can query a vector database, execute a Python script, or call an API to access real-time data or perform a specific task.

How it works

• A user asks a query (e.g., “What is Tesla’s latest stock price?”).

• LLM determines the need for external data and selects appropriate tools.

• It calls an API (for example, a financial data API), retrieves information, and generates a response.

Scenario

In e-commerce, AI agents might use tools to query product inventory databases, check stock levels, and recommend alternatives when items are out of stock, thereby enhancing the customer experience.

Latest Trends

Over the past year, LLM’s API integrations have surged, with platforms such as LangChain now supporting over 250 pre-built connectors. In addition, “Tool Augmented Reasoning” (TAR) technology has gained traction, enabling LLM to chain tools together to perform multi-step tasks, thereby improving task completion rates in applications such as inventory management.

challenge

The use of tools increases the complexity of error handling - failed API calls or flawed scripts can interrupt workflows. The DeepSeek LLM GitHub page also warns that repeated output when interacting with tools can degrade the user experience. To address this, companies are adopting a "tool orchestration layer" that manages tool interactions and provides fallback mechanisms to minimize failure rates.

3. ReAct mode: reasoning + action, dynamic problem solving

The ReAct (Reasoning and Acting) mode combines reflection and tool use, enabling the AI ​​to reason about tasks, take actions using tools, and improve its approach based on the results.

How it works

• The LLM receives a query and reasons about the steps required to solve the query.

• It interacts with external tools or environments to collect information or perform actions.

• Based on the results, it reflects and adjusts its strategy, repeating the cycle until the task is completed.

Scenario

In the field of autonomous robotics, ReAct agents can navigate warehouses by reasoning about paths, using sensors to detect obstacles, and adjusting routes in real time. An NVIDIA case study showed that robots powered by ReAct navigated 20% more efficiently than traditional rule-based systems.

Latest Trends

ReAct is becoming a cornerstone of autonomous driving systems. In the future, ReAct agents will be deployed in smart cities to manage traffic flow by reasoning about sensor data, adjusting traffic lights, and optimizing strategies based on real-time congestion patterns.

challenge

The iterative nature of ReAct can lead to high latency, especially in time-sensitive applications such as real-time trading. In addition, Deeplearning.ai’s “Agent Design Pattern” highlights that ReAct’s reliance on external tools makes it prone to inconsistencies when external tools fail. To mitigate this, the company is adopting a “Tool Health Monitoring” system that can detect and resolve tool failures in advance, thereby reducing downtime by 15%.

4. Planning Mode: Strategic Thinking for Complex Tasks

Planning mode enables AI to break down complex tasks into manageable subtasks, creating a roadmap for execution.

How it works

• A user submits a complex query (e.g., "plan a global product launch").

• The AI ​​generates a plan that breaks down the task into several steps (e.g., market research, campaign design, logistics).

• It executes each step (usually using a ReAct agent) and checks for completion.

Scenario

In supply chain management, a planning agent can optimize transportation routes, ensure timely delivery, and minimize costs by breaking down the task into subtasks: forecasting demand, selecting carriers, and scheduling shipments.

Latest Trends

Planning is gaining traction in the enterprise AI space. A Gartner report predicts that by 2028, 70% of supply chain operations will use planning-based AI agents to optimize logistics.

challenge

Effective planning requires accurate task decomposition, which can be challenging for ambiguous or poorly defined queries. Mistakes in planning can lead to inefficient or incomplete results. To address this, companies are integrating a “plan validation” module that uses reinforcement learning to evaluate and refine plans before execution, increasing success rates by 12%.

5. Multi-agent model: Collaborative AI team

The multi-agent model simulates human teamwork by deploying multiple AI agents, each with a specific role, to complete a task together. For example, agents such as product managers, technical leaders, and DevOps work together to deliver unified results.

How it works

• Users submit queries to a central agent (eg, PM agent).

• The PM delegates tasks to specialized agents (e.g., a DevOps agent for deployment, a technical lead for code review).

• Agents collaborate, share results, and iterate until the task is completed.

Scenario

In financial forecasting, a multi-agent system may include data agents (analyzing historical trends), prediction agents (running ML models), and risk agents (assessing market volatility) to provide traders with comprehensive forecasts.

Latest Trends

Multi-agent systems (MAS) are transforming every industry. Research from MIT shows that through shared learning, agents can exchange knowledge to optimize strategies, reducing the computational cost of MAS by 25%.

challenge

Multi-agent systems can be unpredictable, especially with free interactions and multiple tools. Conflicts between agents (e.g., different priorities) can lead to inefficiencies. To address this, companies are adopting "conflict resolution protocols" that use game theory to coordinate agent goals, thereby minimizing conflicts.