The 7 Foundational Patterns Every Agent-Based System Should Use
In the rapidly evolving world of AI, designing reliable, goal-oriented agents requires more than just prompting a language model. Behind every successful AI assistant, customer service bot, or autonomous agent lies a core architecture based on battle-tested design patterns. If you're building an agent-based system, start with these 7 foundational patterns. These are your primitives—the fundamental building blocks that enable your system to reason, act, adapt, and recover.
1. Prompt Chaining
Concept: Break complex tasks into simpler, sequential prompts where the output of one becomes the input to the next.
Why it matters: LLMs are great at small tasks but falter with complex, multi-step reasoning. Chaining breaks down the complexity and maintains context.
Example Use Case: A travel assistant that first summarizes user preferences, then retrieves flights, then ranks them based on priorities.
Best Practice: Use tools like LangChain's SequentialChain or LangGraph to enforce clear input-output transitions and avoid state confusion.
2. Tool Usage
Concept: Equip your agent with tools (APIs, databases, calculators) and teach it when and how to use them.
Why it matters: LLMs alone can hallucinate or make fuzzy calculations. Tools allow them to anchor reasoning in reality.
Example Use Case: An HR agent that fetches payroll data via an API before responding to a salary query.
Implementation Tip: Define tools with strict input/output schemas and guard against prompt injection. LangChain's Tool interface makes this clean and modular.
3. Memory Management
Concept: Provide short-term memory for ongoing conversations and long-term memory for persistent facts.
Why it matters: Agents need to recall past interactions, preferences, and progress—just like humans.
Example Use Case: A sales assistant that recalls what a lead asked during a previous conversation.
Types:
- Short-term: Session memory (like a chat window)
- Long-term: Vector-based memory or structured storage
Framework Tip: Use LangChain's ConversationBufferMemory for short-term and VectorStoreRetrieverMemory for long-term context.
4. Routing
Concept: Dynamically select which tool, agent, or prompt to use based on the task.
Why it matters: Not all queries are equal. Smart systems must delegate correctly.
Example Use Case: A support agent routes billing queries to a financial tool, and product issues to an FAQ retriever.
Pattern Tip: Implement MultiPromptChain in LangChain or use a decision router with classification models.
5. Exception Handling
Concept: Capture and gracefully manage tool failures, hallucinations, or API errors.
Why it matters: Real-world systems fail. The best ones recover.
Example Use Case: If an API call times out, the agent tries a cached response or apologizes with context.
Best Practice: Use try/except constructs around tool calls and log failures for inspection. Design fallback paths.
6. Goal Setting & Monitoring
Concept: Define clear goals and track progress against them through agent reasoning steps.
Why it matters: Without a target, agents meander. Goal setting aligns behavior with value.
Example Use Case: A recruiting agent aims to shortlist 3 candidates who match job and culture fit criteria.
Design Tip: Embed the goal in the system prompt and use custom validators to check if a sub-goal has been completed.
7. Reflection
Concept: Use the LLM to critique and improve its own prior responses or reasoning.
Why it matters: Reflection enables self-correction and adaptive learning.
Example Use Case: An agent explains its reasoning, spots a mistake, and retries the task with corrections.
How-To: Create a meta-agent that reads the original task + prior output and generates a critique + revision.
Visual Overview: The Agentic Loop
[Goal] → [Prompt Chain] → [Routing] → [Tool Use / Memory Access] → [Result] → [Reflection / Retry / Exception Handling] → [Monitor Outcome]
Conclusion: Start with Seven, Build with Confidence
These seven foundational patterns are not optional—they are the spine of every functional agent. Start with them, master their usage, and you'll be ready to scale into multi-agent orchestration, complex planning, and adaptive intelligence. Whether you're building a customer-facing bot or an internal workflow optimizer, these patterns will keep your agents smart, structured, and resilient.
