Agentic AI Stack • Best Practices

Workflows & Architecture

The AI ecosystem is moving incredibly fast. Startups are consolidating, models are evolving daily, and building production-ready agents requires more than just API calls. Here is the stack of the Agentic AI landscape.

I. The Agentic AI Tech Stack

Understanding where tools fit helps you make informed architectural decisions. Here is the lay of the land from hardware to UI.

Layer Function Key Examples
Harnesses & Apps Scaffolding giving AI autonomy, memory, and UI. Claude Code, Codex, ChatGPT
Agentic Frameworks Structured templates defining how agents think/act. LangChain, LlamaIndex
APIs & Automation External capabilities and trigger workflows. Firecrawl, Perplexity, Zapier, n8n
Knowledge & Memory Vector DBs storing context and history. Pinecone, Weaviate, Mem0
Routing Platforms UI/API layers routing to various models. OpenRouter, Hugging Face
Model Providers The core intelligence engines (LLMs). OpenAI, Anthropic, Google, Meta
Cross-Stack Tools Observability, monitoring, and safety rails. LangSmith, Portkey

II. Model Selection Framework

When picking a model for a specific use case, evaluate your options across these four primary dimensions.

Capability

Does the task require top-tier reasoning (Opus/GPT-4) or simple text processing (open-weight)?

Privacy

Are you handling sensitive, regulated data that requires an on-premise or localized open-weight model?

Cost

Can your budget support expensive API calls per million tokens, or do you need a scalable cheaper alternative?

Latency

Does your application require instant real-time responses (Time to First Token), or is a slight delay acceptable?

III. Prompting & Agentic Strategies

The only way to steer an LLM is through prompting. Swipe through the core techniques below.

01

System vs User

System Prompts: Set persistent rules, domain expertise, and constraints.

User Prompts: The dynamic, task-specific input from the user.

02

Chain of Thought

Force the model to "think" step-by-step before outputting an answer. Decomposing the problem vastly improves reasoning accuracy for complex logic or math.

03

The ReAct Pattern

Reason + Act: The foundation of modern agents. The model reasons about a problem, observes available tools, acts by calling an API, and loops until complete.

04

Avoid Ambiguity

Treat the LLM like a literal-minded junior employee. Provide extremely clear, non-conflicting instructions and use structured output formats (like JSON) to avoid conversational fluff.

IV. The "Vibecoding" Workflow

This is the practical, step-by-step workflow for building applications using an AI coding assistant (like Claude Code or Codex).

1

Define Vision & Architecture

Before touching the coding agent, map out your idea. Know the frameworks (e.g., Streamlit, UV) and the general file structure.

2

Brainstorm with the Agent

Do not let the AI write code immediately. Use a prompt to force it to brainstorm, propose an architecture, and detail its plan.

3

Review the Plan

Act as the technical lead. Review the proposed file structure and interfaces. Make adjustments before giving the green light.

4

Incremental Implementation

Instruct the agent to build the application in stages (e.g., "Build the data upload tab first, then we will test it").

5

Execute, Test, & Heal

Run code locally. If you hit an error, copy the exact terminal message, paste it into a fresh context window, and ask the agent to fix it.