Agentic AI Core
Progressive Guide to Building AI Agents — From Simple Prompts to Multi-Agent Systems
AGENT COMPLEXITY LADDER
L0 → L3 with the build path that gets you there — each step adds capability only when the previous isn't enough.
AGENCY PROGRESSION
Autonomy Ladder - From Human Control to Agent Independence
Human approves every action
Human monitors & intervenes
Agent leads, human oversees
Within defined boundaries
GOVERNANCE MATRIX — COMPLEXITY × AUTONOMY
Which review tier applies when capability (L0-L3) meets independence (HITL → Full)
Risk = autonomy × blast radius, not complexity. An L1 agent with full autonomy and write access needs more governance than an L3 system at HITL.
Cases
Agentic Flows
Context Engineering
Reasoning & Cognition
Tools & Integration
Memory & State
Agent Harness & OS
Design & Architecture
Evaluation & Testing
starts at L0 — deepens per levelExecution Model
Safety & Boundaries
Multi-Agent Orchestration
Human-Agent Collab
Agent Operations
Agents
WHEN TO GO MULTI-AGENT
Don't use five agents for a one-agent problem. Ask these questions first.
ANTI-PATTERNS TO AVOID
Common mistakes that make agent systems unreliable, expensive, or dangerous
One massive while-loop doing everything
→ Separate concerns into distinct steps
Full autonomy with no caps or HITL
→ Cap iterations, tokens, cost, blast radius
Calling a chain with a retry an "agent"
→ Know what you built: chain vs agent
Reaching for LangChain before understanding patterns
→ Learn patterns first, pick framework second
"The window is big" — stuff everything in
→ Attention degrades. Curate what goes in.
Parallel agents modifying the same state
→ Read-heavy agents work; writes need coordination