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

Memory, Harness, and Autonomy: the architectural blueprint that separates a chatbot from a truly autonomous AI agent.

Tags: Artificial Intelligence · Agentic AI · Software Engineering · Google Gemini · LLM

There’s a specific moment in every developer’s journey with AI when “API wrapper” stops being enough. When you’ve shipped the chatbot, tuned the prompt, and still find yourself asking — why does this agent keep losing its mind halfway through a task?

That question got a compelling answer at the Build with AI 2026 workshop, organized by Google Developer Groups Cloud Lahore and hosted at Arbisoft. Amid sessions on the Google Gemini API and real-world generative AI use cases, one technical deep-dive stood apart: a session on Agentic System Architecture.

What followed was a crisp, practitioner-focused framework that I’ve been thinking about ever since. Here’s the full breakdown.

What Makes an AI System “Agentic”?

The term gets thrown around loosely, so let’s ground it. An agentic system is an AI that doesn’t just respond to a prompt — it acts toward a goal over time, using tools, retaining context, and self-correcting along the way. The difference between a language model and an agent is roughly the difference between a calculator and an accountant.

“An agent without memory is amnesiac. An agent without a harness is dangerous. An agent without autonomy is just expensive autocomplete.”

The session was organized around three foundational pillars. Master all three, and you have a system that can genuinely operate in the real world. Miss one, and you’re one long task away from a failure mode.

Pillar 1 — Memory (Persistence)

The agent’s ability to retain context, recall prior decisions, and build on past interactions. Not just a chat history — a structured, retrievable knowledge system.

Lazy Loading

The first concept introduced was lazy loading for memory. The instinct when building agents is to dump everything into the context: full conversation history, all instructions, every relevant document. This is exactly wrong.

Lazy loading means memory is fetched on demand, not pre-loaded. The agent retrieves what it needs when it needs it — keeping the active context small, precise, and relevant. Think of it as the difference between carrying your entire library everywhere versus knowing where to find a book when you need it.

Progressive Disclosure — The L1 / L2 / L3 Model

The session introduced a layered memory architecture called Progressive Disclosure. Rather than one flat context window, memory is organized into three distinct tiers:

L1 — Router The entry point. Classifies the incoming query and decides which memory layers need to be activated. It’s the intent layer — minimal, fast, focused on routing, not content.

L2 — Instructions The behavioral layer. Once the router knows what type of task this is, L2 loads the relevant behavioral instructions, persona, rules, or step-by-step guidance. Only the instructions applicable to this specific query are pulled in.

L3 — Data Vault The knowledge layer. Specific facts, documents, user history, database results, or any grounding data. Retrieved selectively based on what L1 and L2 determined is needed.

The elegance of this model is that it’s additive. Each layer adds only what the current task requires. The result is a context window that stays lean, focused, and interpretable — rather than an overwhelming blob of tokens the model has to sift through.

Practical insight: Most agent failures aren’t model failures — they’re context management failures. Too much irrelevant information causes the model to hallucinate, contradict itself, or simply lose track of the goal. Progressive Disclosure directly addresses this.

Pillar 2 — Harness (Tools, Rules & Governance)

A harness is the scaffolding around an agent. If memory is the brain, the harness is the job description, the company policy, and the compliance department all rolled into one.

Skills Discrete, composable capabilities. Each skill is a unit of work the agent knows how to do — calling an API, writing to a file, running a query, using a browser. Skills should be modular and independently testable.

Rules Behavioral guardrails. Explicit constraints on what the agent can and cannot do. Rules are the difference between an agent that helps users and one that accidentally deletes production data.

Governance Audit trails, approval gates, and oversight mechanisms. For high-stakes actions, governance ensures a human reviews before execution. This is what makes agentic systems enterprise-deployable.

“An agent without guardrails is just chaos with an API key. Skills + Rules + Governance = an agent you can actually trust in production.”

Pillar 3 — Autonomy (Logic, Eval & Control)

Git Worktrees

One of the most practical ideas in the session. Git worktrees allow an agent to work on multiple branches of a codebase simultaneously, in isolation. For coding agents, this means parallel task execution without interference — different branches for different sub-tasks, all running concurrently. The agent isn’t blocked; it branches.

Feedback Loops in the Harness

Autonomy without feedback is just running blind. The session described closing the feedback loop by routing agent outputs back into the harness as new inputs. Each action produces an observation. That observation becomes context for the next decision. This iterative loop is what allows an agent to self-correct rather than barrel forward on a mistaken assumption.

Eval — Mutation Testing & BDD

Rather than relying on vibe checks (“looks good?”), the framework proposes rigorous evaluation:

Behavior-Driven Development (BDD) Define what “correct” looks like in human-readable scenarios before the agent runs. The agent’s output is then tested against these scenarios. Did it actually solve the problem as specified?

Mutation Testing Intentionally introduce small changes (mutations) to the agent’s environment or outputs and check whether the eval catches them. If your tests pass even with a broken agent, your tests are useless.

The Critical Warning — Context Drift

⚠️ Keep your context window as small as possible. Not as large as possible — as small as possible. As agents operate over long sessions, accumulated context causes behavioral drift. The model’s responses begin to be influenced by stale, contradictory, or irrelevant earlier content. Prune aggressively. The best context is the minimum viable context.

This ran counter to much of the industry narrative around “longer context = better agents.” The insight is that context length is a budget, not a feature. Spending it carelessly is the fastest way to produce an agent that confidently does the wrong thing.

Resources — Context Mode & Community Tooling

The session pointed to several open-source projects that embody these ideas in practice:

• https://github.com/mksglu/context-mode
• https://github.com/ChromeDevTools/chrome-devtools-mcp
• https://github.com/addyosmani/agent-skills
• https://github.com/gemini-cli-extensions/conductor

Also highlighted: Browserbase and auto-browser tooling for SQA skills — enabling agents to autonomously run web-based quality assurance tests. When paired with BDD evals, this closes a loop that most engineering teams leave wide open.

Putting It All Together

The three-pillar model is a diagnostic framework as much as an architectural one. When an agent misbehaves, you can almost always trace it to one of three root causes: it’s remembering wrong things (Memory), it’s doing things it shouldn’t (Harness), or it’s not checking its own work (Autonomy).

Asking “how does this system handle Memory, Harness, and Autonomy?” is now my first question when evaluating any agentic product — or auditing one I’ve built myself.

“Precision beats volume. The best agent isn’t the one with the most context. It’s the one that knows exactly what context to use.”

Events like Build with AI 2026 are a reminder that the most useful AI concepts don’t come from papers alone — they come from practitioners building things that need to actually work. Thank you to GDG Cloud Lahore and Arbisoft for creating that space.

This article is based on session notes from Build with AI 2026, organized by Google Developer Groups Cloud Lahore at Arbisoft, Lahore, Pakistan. April 2026.

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