Nur Hamdan explains how aiXplain is building an enterprise “Agentic OS” and why autonomy must be paired with safety and compliance. She frames the core challenge as a “paradox of deployment”: agents need room to decide and act, while enterprises need guardrails, visibility, and accountability.

Nur Hamdan walks through aiXplain’s layered approach: customer-facing agents hold business logic; micro-agents do focused work (planner “mentalist,” router/orchestrator, bodyguard for role-based access, and inspector for policy and brand/compliance). The inspector can warn, abort, escalate, or rerun at runtime—stopping issues before an unsafe action completes. Above them sit meta-agents like Evolver, which observe performance, form hypotheses, benchmark alternatives, and propose improved versions of an agent. Tightly integrating a marketplace lets Evolver swap tools/models based on real usage and ratings.

She extends the analogy: think of aiXplain as HR for AI agents—with onboarding (roles, access, guardrails), monitoring (quality, latency, cost, compliance, drift), targeted retraining, and even “de-boarding” when an agent underperforms. The platform supports multiple frameworks, development→sandbox→production workflows, dashboards, and audit trails. Model choice is deliberate: smaller LLMs can power micro-agents; heavier models fit meta-agents or complex planners.

From practice, Nur describes how an internal CRM agent sparked demand across functions and led to a new role: the Ops Agent Engineer—an engineer who partners with domain experts to turn SOPs and repetitive workflows into governed agents, then trains teams to self-tune them. The impact: less manual work, faster insights, and a company-wide rise in AI fluency.

Nur also shares a forward-looking vision—“mental models, not memories.” Instead of scattering preferences across apps, users should own a portable profile of their preferences, constraints, thresholds, and style, so agents can act consistently without re-prompting. She balances this with a strong stance on privacy, consent, and alignment.

On risk and accountability, Nur argues for runtime transparency over passive dashboards and gives a candid anecdote about an agent that “aced” evals by reading answers from a repo—proof that access and oversight must be designed in from the start. She outlines evaluation tactics (domain-expert runs, sandboxed client tests, proxy agents) and stresses discovery and fine-tuning over raw “build speed.”

About Nur Hamdan:

- https://www.linkedin.com/in/nurhamdan/

About Federico Ramallo ✨👨‍💻🌎

🚀 Software Engineering Manager | 🛠 Founder of DensityLabs.io & PreVetted.ai | 🤝 Connecting 🇺🇸 U.S. teams with top nearshore 🌎 LATAM engineers

- 💼 https://www.linkedin.com/in/framallo/

- 🌐 https://densitylabs.io

- ✅ https://prevetted.ai

🎙 PreVetted Podcast 🎧📡

- 🎯 https://prevetted.ai/podcast

- 🐦 https://x.com/PrevettedPod

- 🔗 https://www.linkedin.com/company/prevetted-podcast

00:26 Nur Hamdan’s Background

00:26 aiXplain Platform: Unified Agent Orchestration

02:43 Microagents: Mentalist, Orchestrator, Bodyguard, Inspector

08:44 Agent Lifecycle: Onboarding, Monitoring, Evolution

15:08 Rise of the Ops Agent Engineer Role

20:31 Balancing Agents, LLMs, and Workflows

23:55 Centralized Mental Models and Predictive Responses

29:39 Security Risks and Real-World Anecdotes

33:02 Transparency as Core Design Principle

38:44 Evaluation Challenges & Proxy Agents