The Three Pillars of Agentic AI Systems — A Developer’s Field Notes from Build with AI 2026

2026-04-26 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Intermediate, medium

Summary

A technical deep-dive from the Build with AI 2026 workshop, organized by Google Developer Groups Cloud Lahore, introduced a framework for building robust agentic AI systems based on three foundational pillars: Memory, Harness, and Autonomy. An agentic system acts toward a goal over time, using tools, retaining context, and self-correcting. The Memory pillar emphasizes "lazy loading" and a "Progressive Disclosure" layered architecture (L1-Router, L2-Instructions, L3-Data Vault) to keep context windows small and relevant. The Harness pillar focuses on defining agent capabilities through modular "Skills," enforcing behavioral "Rules," and ensuring "Governance" with audit trails and approval gates. The Autonomy pillar covers mechanisms like Git worktrees for parallel task execution, feedback loops for self-correction, and rigorous evaluation using Behavior-Driven Development (BDD) and Mutation Testing. A critical warning highlights that context drift from overly large context windows is a major cause of agent failure.

Key takeaway

For AI Engineers and Architects designing autonomous systems, prioritize context management by implementing layered memory architectures like Progressive Disclosure. Your focus should be on keeping context windows as small and relevant as possible to prevent behavioral drift and improve reliability. Integrate robust harnesses with explicit skills, rules, and governance, and establish rigorous evaluation methods like BDD and mutation testing to ensure your agents perform predictably and safely in production environments.

Key insights

Effective agentic AI systems require structured memory, controlled actions, and self-correction for real-world operation.

Principles

• Context management prevents agent failures.
• Guardrails are essential for production agents.
• Rigorous evaluation validates agent behavior.

Method

Implement a layered memory (L1-Router, L2-Instructions, L3-Data Vault) with lazy loading. Define modular skills, explicit rules, and governance. Integrate feedback loops and use BDD/Mutation Testing for evaluation.

In practice

• Use lazy loading for agent memory.
• Organize memory into L1/L2/L3 tiers.
• Apply Git worktrees for coding agents.

Topics

• Agentic AI Systems
• AI Memory Management
• Progressive Disclosure
• AI Harness
• AI Autonomy

Code references

• mksglu/context-mode
• ChromeDevTools/chrome-devtools-mcp
• addyosmani/agent-skills
• gemini-cli-extensions/conductor

Best for: AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.