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100 Days of Agentic Workflows

Master autonomous agent design, orchestration, and deployment across 100 days. Progress from foundational concepts to building production-ready agentic systems.

Level: beginnerCreated: 1/23/2026

This plan is public and visible in the Learning Plans Library.

Prerequisites

•Basic Python programming
•Understanding of APIs
•Familiarity with LLMs

Learning Outcomes

✓Design autonomous agents with goal-driven behavior
✓Implement multi-agent orchestration patterns
✓Build feedback loops and memory systems
✓Deploy agents in production environments
✓Create complex workflows with reasoning and planning

Weekly Breakdown

Week Goals

• Understand agent architecture
• Learn core concepts

Day 1

Agentic Systems Intro

Understand what agents are and how they differ from traditional programs.

Tasks

→Define autonomous agents
→Identify key agent characteristics
→Explore real-world examples

Day 2

Agent Perception Loop

Learn the sense-think-act cycle that drives agent behavior.

Tasks

→Study perception mechanisms
→Analyze decision-making stages
→Map action execution

Day 3

Goal-Driven Behavior

Explore how agents define and pursue objectives autonomously.

Tasks

→Define goal specification
→Learn objective functions
→Practice goal decomposition

Day 4

Agent Environment Interaction

Understand how agents interact with and respond to their environment.

Tasks

→Study environmental states
→Learn observation patterns
→Practice feedback interpretation

Day 5

Agent Types & Architectures

Compare different agent architectures and their use cases.

Tasks

→Compare reactive agents
→Study deliberative agents
→Analyze hybrid approaches

Day 6

Decision-Making Frameworks

Learn how agents make decisions under uncertainty.

Tasks

→Study decision trees
→Learn probability reasoning
→Explore utility functions

Day 7

Planning & Reasoning

Understand how agents plan sequences of actions.

Tasks

→Learn action planning basics
→Study state space search
→Practice simple planning