Architecting the Agent OS

Dhruv Aggarwal — Sat, 16 May 2026 05:53:31 +0000

Deploying autonomous agents without a management layer is a significant reliability risk. While an LLM provides the "intelligence," it lacks the operational constraints required for production. Without an orchestration layer—an "Agent OS"—you are essentially running unconstrained code with access to your critical infrastructure.

To move beyond unpredictable prototypes, we need to treat Agent orchestration as a systems design problem. A robust Agent OS must implement these six primitives:

Scheduler & Orchestrator: Manages task prioritization and resource allocation to prevent race conditions and ensure high-priority tasks aren't pre-empted by recursive loops.
Memory Manager: Solves the context window limitation by bridging Short-Term Memory (current session state) with Long-Term Memory (vector databases/RAG) to prevent repetitive loops and state loss.
Tool Manager: Implements a secure execution layer. Instead of granting direct API access, it provides a sandboxed environment (e.g., isolated containers) to prevent catastrophic failures like accidental database drops.
Identity Manager: Enforces the Principle of Least Privilege (PoLP) using ephemeral tokens and certificates. This ensures that an agent's identity is scoped to a specific task and expires immediately after execution.
Observability: Provides deterministic tracing for non-deterministic outputs. Every decision, tool call, and state change must be logged to allow for post-mortem debugging and auditing.
Guardrails & Governance: A dual-layer defense. Technical guardrails filter malicious injections and profane outputs, while governance frameworks enforce "Human-in-the-Loop" (HITL) triggers for high-stakes mutations.

The goal is to shift the paradigm from "hope it works" to a system defined by predictability, security, and trust.

For those of you moving agents into production: Which of these layers is currently your biggest point of failure—memory persistence or secure tool execution?

Why your infra is the silent bottleneck in your AI systems?

Dhruv Aggarwal — Fri, 08 May 2026 11:00:40 +0000

Getting high-quality responses from an LLM is rarely a model problem; it is almost always an infrastructure problem.

Frontier models have the reasoning capabilities, but they are limited by the quality and accessibility of the context they are given. This is where Context Engineering—the intersection of RAG and Prompt Engineering—becomes the critical path.

The challenge is that enterprise context is fragmented. It's spread across DBs, SaaS platforms, and on-prem systems, varying between structured and unstructured, and heavily guarded by RBAC.

To solve the context bottleneck, I view the architecture through four pillars:

Connected Access: Use zero-copy federation. Access data where it lives rather than creating unfederated copies. This provides the LLM with immediate visibility.
Knowledge Layer: Implement entity resolution and institutional knowledge mapping on top of raw data to provide actual meaning.
Precision Retrieval: Prioritize data by intent, role, and policy. More context does not equal more knowledge; precision ensures relevancy.
Runtime Governance: Apply dynamic checks to determine if a specific data source should be queried based on the user's permissions. This makes the system defensible.

Ultimately, an AI system is only as effective as the context it can retrieve.

How are you handling context retrieval and RBAC in your current AI pipelines?

Forem: Dhruv Aggarwal

Architecting the Agent OS

Why your infra is the silent bottleneck in your AI systems?