Chapter 37: People & HR

Teaching Aid

"Every HR team I have ever worked with is simultaneously under-resourced and drowning in repetitive work. They spend 60% of their time answering the same ten questions — 'What is our parental leave policy?' 'How do I request a reference letter?' 'Where do I find the performance review template?' — and the other 40% doing high-judgment work that only humans can do: navigating a difficult termination, mediating a team conflict, assessing a candidate's cultural fit. The tragedy is that the 60% is exhausting and adds no value, and the 40% is chronically under-invested because the 60% has consumed the time and energy that the 40% deserved."

HR operations fail in two places. First, explicit knowledge — written policies, handbooks, process documents — is scattered across shared drives, HRIS systems, and the institutional memory of long-tenured employees. Second, tacit knowledge — the unwritten understandings that govern how things actually work — lives in people's heads and evaporates when they leave.

This chapter builds the intelligence layer that solves both problems. You will deploy 14 skills across two plugins and 4 persistent agents that transform HR operations from reactive administrative overhead into continuous, anticipatory people intelligence.

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What You'll Learn

By the end of this chapter, you will be able to:

• Diagnose the three structural failures in HR operations (information routing burden, process repetition, knowledge evaporation) and explain how AI agents address each one
• Deploy the two-plugin architecture (official human-resources with 9 skills + custom hr-operations with 5 skills and 4 agents) and configure it for your organisation using hr.local.md
• Build a searchable HR knowledge base with policy self-service, escalation protocols, and weekly operational intelligence reports
• Automate onboarding workflows from pre-boarding through the 90-day review, with role-specific success criteria at each milestone
• Write inclusive job descriptions, generate structured interview rubrics, and draft employment documents with jurisdiction-specific compliance
• Structure performance reviews that convert vague manager impressions into specific, evidenced, actionable feedback
• Run internal talent assessments across six dimensions and design succession plans with honest career development conversations
• Capture institutional knowledge before it walks out the door, using structured interview guides and knowledge article frameworks
• Deploy four persistent agents that monitor policy currency, orchestrate onboarding, capture departure knowledge, and answer employee questions 24/7

Lesson Flow

Lesson	Title	Duration	What You'll Walk Away With
L01	The Institutional Memory Problem	40 min	Why HR operations fail: information routing burden, process repetition, and knowledge evaporation
L02	Your HR Operations Stack	50 min	Both plugins installed, hr.local.md configured, all 14 commands accessible
L03	Policy Lookup & Employee Self-Service	40 min	Plain-language policy summaries with citations, escalation paths, and self-service patterns
L04	The HR Knowledge Base Agent	45 min	20-entry FAQ database, query classification, warm handoff patterns, weekly intelligence report
L05	Onboarding — The First 90 Days	40 min	Complete onboarding programme with pre-boarding checklist, 30-60-90 plan, and manager briefing
L06	Job Descriptions & Interview Preparation	40 min	Three inclusive job descriptions (junior, mid, senior) and a structured interview rubric
L07	Offer Letters & Employment Documents	35 min	Offer letter, reference letter, and employment verification — each jurisdiction-compliant
L08	Performance Reviews Without Bureaucracy	40 min	Structured performance review with evidenced strengths, behavioural development areas, and career plan
L09	Compensation, Talent & Org Planning	45 min	Internal talent assessment for two candidates, compensation benchmark, succession plan
L10	Capturing Institutional Knowledge	50 min	Knowledge capture plan, interview guide, and structured knowledge articles for a departing expert
L11	Offboarding & Knowledge Transfer	35 min	Complete offboarding plan with handover, exit interview design, and knowledge transfer automation
L12	Persistent Agents — Orchestrator & Maintenance	40 min	Onboarding orchestrator and policy maintenance agent configured and running
L13	People Analytics & Agent Operations	40 min	Quarterly people report, recruiting pipeline analysis, and HR intelligence dashboard design
L14	Capstone — The Full Employee Lifecycle	90 min	Full employee lifecycle executed: hire, onboard, develop, review, promote, offboard
L15	Quick Reference & Central Insights	20 min	All commands, all agents, key tables, and the chapter's central insight

Agent Output Taxonomy

All HR agent outputs carry a sensitivity label:

Label	Content Types	Handling
ROUTINE	Policy summaries, job descriptions, onboarding plans, general queries	Standard output — no special handling
CONFIDENTIAL	Offer letters, salary details, performance reviews, talent assessments, reference letters	Handle per data protection policy
SENSITIVE PERSONAL DATA	Medical, disciplinary, grievance, termination documentation	Never auto-generated — always escalate to named HR professional

Case Studies

Character	Role	Company	Jurisdiction	Appears In
Ayesha Raza	Senior Data Analyst (new hire)	EdTech, Karachi	Pakistan	L01, L02, L05, L07, L10, L14
Omar Farooq	Head of Analytics (manager)	EdTech, Karachi	Pakistan	L05, L08, L10, L14
Bilal Ahmed	Software Engineer	EdTech, Karachi	Pakistan	L08, L09
Zara Hussain	Senior Data Engineer	EdTech, Karachi	Pakistan	L09
Marcus Chen	Product Marketing Manager (new hire)	Technology, London	UK	L06, L07
Priya Kapoor	VP Marketing (manager)	Technology, London	UK	L07

Chapter Contract

By the end of this chapter, you should be able to answer these five questions:

1. What are the three structural failures in HR operations, and how does a two-plugin architecture (9 official + 5 custom skills) address each one?
2. How does the HR Knowledge Base Agent distinguish between policy queries it should answer directly and individual situations it must escalate — and why is this distinction the most critical design decision in the system?
3. How does the /match skill assess internal candidates across six dimensions, and why does motivation matter as much as capability in succession planning?
4. What is the difference between proactive and reactive institutional knowledge capture, and why does the offboarding-knowledge-agent's risk scoring determine the depth of capture?
5. How do the four persistent agents (Knowledge Base, Onboarding Orchestrator, Policy Maintenance, Offboarding Knowledge) work together to provide continuous people operations intelligence?

Prerequisites: Cowork Access

This chapter requires Cowork (set up in Chapter 28) and two plugins.

1. Install the official Human Resources plugin. In the Cowork sidebar: Customize -> Browse plugins -> search "Human Resources" -> click Install.
2. Install the HR Operations plugin. In the Cowork sidebar: Customize -> Browse plugins -> Personal -> click + -> Add marketplace from GitHub -> enter https://github.com/panaversity/agentfactory-business-plugins -> find HR Operations -> click Install.
3. Connect a working folder for practice files, same as Chapter 28.

After Chapter 37

When you finish this chapter, your perspective shifts:

1. You see HR operations as an information problem. Every repeated policy question, every late onboarding, every knowledge gap was a signal that existed in the data before it became a crisis. The question is whether anyone — or any agent — was watching.
2. You have a working two-plugin stack. 14 skills and 4 persistent agents covering the full employee lifecycle — from job description to offboarding — are installed, configured, and deployable.
3. You understand the boundaries. The agents answer policy questions, generate documents, classify talent, and capture knowledge. They do not navigate terminations, mediate conflicts, or make promotion decisions. These boundaries are encoded in every skill file.
4. You can extend. The knowledge capture framework applies to any domain where expertise concentrates in individuals. The onboarding orchestrator pattern works for any milestone-driven workflow. The sensitivity labelling system applies wherever AI handles personal data.

Start with Lesson 1: The Institutional Memory Problem.