DS
Diwesh Saxena

Research & Publications

Practitioner-focused research on AI agents, HRTech discoverability, hiring fairness, and health tech — grounded in production systems, not lab demos.

Technical Brief
Site preview

1 March 2026

Agentic Workflows in Senior-Living Tech

Senior-living operators face staffing pressure and rising resident expectations. Agentic workflows can handle routine requests — dining, maintenance, activities — but vulnerable populations demand stricter trust boundaries than typical B2B SaaS. This brief covers voice-agent architecture, mandatory human escalation for health intents, and audit patterns from OEAT pilot deployments.

  • Routine vs. sensitive intent routing must be explicit in architecture, not prompt-only.
  • Voice adds latency and accessibility benefits but increases mis-hear risk — always offer human fallback.
White Paper (Zenodo)
Published

1 September 2025

AI Agent Failure Modes in Production Systems

Production AI agents fail in predictable ways — not because models are weak, but because orchestration, tools, and guardrails are under-designed. This white paper documents six failure modes observed across HRTech and HealthTech deployments, and proposes a three-layer resilience model: input guardrails, runtime circuit breakers, and post-hoc evaluation replay.

  • Six recurring failure modes: tool timeout cascades, silent hallucination, context bleed, wrong-entity merges, prompt injection, and drift without detection.
  • A three-layer model — guardrails, runtime checks, eval replay — reduced incidents 40% in a live ATS agent deployment.
Publication (Zenodo)
Published

20 January 2025

From Talent Pools to Talent Graphs: Rethinking Discoverability in Closed Consultant Networks

Closed consultant networks cannot rely on open marketplace search. Talent pools flatten relationships; talent graphs preserve context — who worked with whom, on what skills, under which constraints. This publication compares SQL search vs. graph traversal for niche staffing requests and outlines privacy-preserving integration patterns for HR tech platforms.

  • Linear talent pools break down for multi-skill, relationship-aware searches.
  • Graph models encode engagements, referrals, and skill adjacency without exposing raw PII.
Publication (Zenodo)
Published

15 June 2024

Mitigating Bias in AI-Powered Recruitment

AI-powered hiring tools can amplify historical bias unless fairness is engineered into the release process. This paper outlines evaluation frameworks for candidate ranking — demographic parity checks, explainability requirements, and recruiter override loops — drawn from production ATS work at Noble House Consulting.

  • Bias mitigation is a release gate, not a one-time audit.
  • Explainable ranking signals improve recruiter trust and catch drift early.

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