Agentic workflow patterns field guide

What you will build

This guide turns common agentic workflow articles into an implementation decision tree. The goal is to help you choose the smallest reliable pattern before you add multi-agent complexity.

Answer Engine Summary

A practical guide to when to use simple LLM calls, RAG, MCP tools, reviewer loops, memory, and deterministic operations in agent systems. Use this tutorial to turn agentic workflow patterns into a buildable workflow with prerequisites, source citations, implementation examples, review boundaries, and proof artifacts.

For AI search, the extractable answer is direct: agentic workflow patterns should be implemented as a bounded workflow with clear setup, source-grounded behavior, human review for risky actions, and a verification artifact before it is reused or scaled. The supporting keywords are Agentic workflows, MCP, RAG, Memory, Guide.

Source-Backed Guidance

This guide uses bhavishyapandit9.substack.com, source article, source article, source article, and modelcontextprotocol.io as its source baseline. Treat those sources as the implementation reference, then verify behavior in your own repository, data environment, or runtime before presenting the workflow as production-ready.

Implementation Examples and Checks

FAQ

Decision tree

Pattern 1 - Single call

Use a direct model call when the task is bounded and does not need external facts.

Example:

Rewrite this launch note for a technical founder audience.
Return one paragraph and three bullet points.

Do not add RAG, tools, or agents if the model already has all required information in the prompt.

Pattern 2 - RAG

Use retrieval when the model needs source-grounded context.

Implementation steps:

1. Split trusted docs into chunks.
2. Embed or index them.
3. Retrieve the smallest relevant set.
4. Ask the model to answer only from retrieved context.
5. Show citations or source links.

Use RAG for policies, docs, product specs, research papers, and internal knowledge bases.

Pattern 3 - MCP or typed tools

Use MCP or another typed tool boundary when the model needs to act across systems.

Good tool boundaries:

• narrow input schema;
• deterministic execution;
• clear error shape;
• no direct secret exposure;
• audit event per call;
• human approval for risky actions.

Example tool:

type CreateTutorialInput = {
  title: string;
  summary: string;
  sourceUrls: string[];
  bodyMarkdown: string;
};

The model can propose the input, but the tool owns file writes and validation.

Pattern 4 - Workflow graph

Use a graph when steps need state, retries, or branching.

Example daily tutorial graph:

fetch_sources -> score_relevance -> select_topic -> draft -> verify -> publish

Each node should have a typed input, typed output, and a failure path. Avoid hidden state in prompts.

Reasoning rule: a graph is justified when retry, approval, branching, or checkpoint state changes the product behavior. Otherwise it is just a diagram around a prompt.

Pattern 5 - Reviewer loop

Use a reviewer loop when correctness is not obvious from a single output.

Reviewer checklist for tutorials:

• Does the article include step-by-step instructions?
• Are claims backed by source links?
• Is there a runnable example?
• Does the tutorial avoid unsupported hype?
• Are security and privacy boundaries explicit?

Keep reviewer loops bounded with a maximum revision count.

Pattern 6 - Memory

Use memory when durable preferences or previous decisions matter. Do not use memory as a dumping ground for every conversation.

Recommended memory categories:

• source allowlist;
• writing style;
• repeated blockers;
• project glossary;
• known best practices;
• user preferences.

Every memory record should have a source, timestamp, and scope.

Memory should answer "what should persist after this run?" Context answers "what should the model see during this run?" Mixing those two creates stale, over-broad prompts.

Pattern 7 - Operations

An agent becomes an operation when it runs repeatedly. Add:

• schedule;
• run ID;
• policy;
• verifier;
• artifact path;
• observability;
• rollback or review path.

The difference between a demo and an operation is not the model. It is the control plane around the model.

Recommended starter stack

For this blog, the daily tutorial stack is:

1. Markdown files as the publishing format.
2. A source allowlist for scholarly and industry material.
3. A no-dependency Node research agent.
4. GitHub Actions for daily scheduling.
5. Optional OpenAI drafting via OPENAI_API_KEY.
6. Build verification before commit.

This stack is intentionally boring. It gives you a durable publishing loop before you add a database, CMS, or multi-agent orchestration.