AI Agent Architecture: Design Effective Agentic Systems

Compare ai workflows and ai agents, showing how workflows follow predefined steps with llms, while agents independently decide actions, ask clarifying questions, check inventories, and generate promo codes.

Explore the core building blocks of ai agents, including perception, reasoning and planning, memory and retrieval, action and execution, feedback and adaptation, evaluation and monitoring, and governance and safety guardrails.

Assess when to apply an agentic workflow versus a fixed sequence, aiming for the simplest solution, and deploy flexible AI agents for open-ended, real-time troubleshooting and adaptive decision making.

effective AI workflows and agent systems require thoughtful architecture to scale, ensure reliability, and maintain control, balancing prompts, memory, routing, latency, costs, and multi-step tasks.

Explore prompt engineering to control an LLM with clear instructions, laying the foundation for building systems, workflows, and agents that can act, reason, and improve over time.

Design a system prompt that defines an agent's role, tone, and limitations to shape input interpretation and responses, including clear objectives and what not to do for ethical safety.

Explore prompt engineering techniques for ai agent systems, including zero-shot, one-shot, and few-shot prompting. Balance context window and prompt size to optimize model performance using 2–3 examples when suitable.

Explore how positive and negative prompting shapes AI outputs by using do and don't instructions, crafting a friendly tone, three case studies, and structured numbered steps to improve model behavior.

Explore chain-of-thought reasoning as a prompt engineering technique that instructs AI models to explain their reasoning and outline the steps to reach a conclusion, increasing transparency for engineers.

Explore architecture patterns for critical agentic workflows, from the augmented Lem foundation and prompt chaining to advanced orchestrator, worker, and evaluator optimizer models, with predefined step-by-step guidance for reliable systems.

The augmented language model (LM) extends a powerful model with retrieval, external tools, and memory to search data, access APIs, and remember user preferences for precise, multi-step portfolio insights.

Break down large tasks into smaller, manageable steps using prompt chaining, guiding sequential LM calls with gates to ensure accuracy while managing latency and cost.

Route queries to specialized language models using an LLM dispatcher. This routing approach balances cost and latency by directing simple queries to small models and complex ones to advanced models.

Discover how parallelization speeds AI responses by distributing subtasks across multiple LMs and aggregating results. Learn when to use sectioning or voting with concrete examples.

Breaks user queries into dynamic subtasks using an LM orchestrator, delegates to specialized worker LMs, and synthesizes the results into a cohesive solution.

Master the evaluator-optimizer pattern, where a generator lm and an evaluator lm iteratively refine outputs until they meet brand, social media, marketing, and ethical guidelines.

Explore single AI agent architectures like reflection and react, and learn when to use them. Discover how choosing the right architecture expands the capabilities of autonomous agent systems.

Explore single agent architectures like reflection and react, emphasizing planning as a before action step. See how self-correction and the cycle of planning, acting, reevaluating, and correcting prevent infinite loops.

Apply reflection as a single agent architecture: receive a request, generate an initial response, then self-reflect, plan, and revise through iterations until quality meets criteria.

Adopt the react (think-do) approach, where agents reason before acting, tackle multi-step tasks, and learn from outcomes, as shown by scheduling a meeting.

Explore the reflection agent design pattern, where task-specific reasoning is paired with self-reflection summaries stored for long-term memory. Enable cumulative learning and adaptive behavior through persistent memory of past reflections.

Explore planning as a foundational cognitive skill for single and multi-agent systems, and compare plan and solve, tree of thought, and task decomposition to break down complex goals.

Learn how AI agents decompose complex tasks into 3–7 subtasks and map dependencies. Apply stop criteria to prevent scope creep and finish within cost and time constraints.

Evaluate when planning helps or hinders task execution, from travel to daily needs. Incorporate a planning component in ai systems that outlines objectives, resources, constraints, and a roadmap before acting.

Plan and solve teaches a two-stage approach: plan by assessing the task, resources, and constraints, then select tools, sequence steps, and verify results, replanning as needed.

Riwu separates planning, execution, and solving, creating a fixed plan and executing it without reassessment to save tokens; it suits static environments, unlike plan and solve which adapt to changes.

Tree of thought merges planning with exploratory search to consider multiple future states and select the best path for complex problems, though it uses more tokens and can be slower.

Explore multi-agent system designs after covering prompt engineering and planning patterns. Examine real world use cases for multiple agents over a single one and compare strengths and trade-offs across architectures.

Decide between a single-agent design and multi-agent systems based on the task and resources; single agents handle simple processes, while multi-agent setups enable parallel work and faster results.

Explore vertical and horizontal multi-agent architectures, comparing a single leader with task assignment and low latency to a flat, volunteer-driven collaboration that emphasizes group thinking and openness.

Prioritize focused task execution with a single agent to avoid distractions, while robust prompt engineering and defined access permissions balance information flow in horizontal and vertical multi-agent architectures.

Explore vertical multi-agent architecture with the supervisor pattern, where a decision maker exercises top-down control, delegates tasks, gives feedback, reviews outputs, and breaks work into subtasks to deliver cohesive responses.

The hierarchical team architecture expands supervisor roles into layered managers, coordinating specialized teams (copywriting, graphic design, budgeting, and media strategy) to scale, maintain quality, and ensure smooth information flow.

Explore dynamic teams as a flexible pattern that assembles, disassembles, and reconfigures agents, while maintaining an authority layer that assigns tasks and typically follows a vertical structure.

Explore how your designed AI agent runs in production, focusing on asynchronous task execution, cost and latency metrics, and strategies for error handling and recovery.

Explore asynchronous task execution and the difference between single and multi-agent systems, highlighting parallelization benefits, latency reduction, and scalability, while noting when dependency limits async gains.

Evaluate latency and cost in live agent systems by balancing model interference and orchestration overhead, exploring parallelization, scalability, redundancy, and prompt/caching optimizations.

Design robust error handling and recovery in agent architectures to detect failures, implement retry logic, route to alternative LM, and keep the user experience smooth despite outages.

Explore how agents use memory to remember past interactions and improve results. Examine short term context buffer, vector store rag, and entity level memory for retaining details about entities.

Use a short-term context buffer to retain the last ten messages for fast, lightweight responses within a memory window, with overflow signaling a need for long-term context via vector stores.

Explore how vector store rag enables long-term memory for agents by converting knowledge into vectors, storing them in a vector database, and retrieving data to enhance large language model responses.

Entity level memory lets chatbots learn about you, store your preferences and past interactions in a knowledge repository, and personalize responses over time, such as weather in Celsius.

Explore self-reflection and human-in-the-loop feedback in agent architecture, as agents evaluate actions, adjust outputs, and leverage human guidance to form feedback loops that boost reliability.

Explore how self-reflection and self-discovery planning enable agents to solve problems through sequences of small reasoning steps, adapting strategies to each task for more efficient, flexible decision making.

Engage humans to guide AI agents, correcting course and improving alignment through feedback. Safeguard reasoning by combining self-reflection with human oversight to prevent dead ends and repeated errors.