[New Mission] Operative NextGen: Process resumes and applications

[aprildunnam]

[New Mission] Operative NextGen: Process resumes and applications

Summary

Author the end-to-end resume intake mission. Learners upload a resume through native file handling, extract structured candidate data, query job roles and criteria through Dataverse MCP, match the candidate, and create Candidate, Resume, and Job Application records.

This consolidates classic multimodal prompts and Dataverse-grounding flows into one modern capability: native file understanding plus MCP-backed data operations. Duplicate checks found no issue proposing this consolidated mission.

Proposed mission

Field	Value
Section	Operative NextGen
Folder	docs/operative-v2/05-resume-intake-matching-applications/
Title	Mission 05: Resume Intake, Matching, and Applications
Operation codename	OPERATION SMART INTAKE
Difficulty	3
Time	90 minutes

Scenario

A recruiter uploads a resume and asks the Hiring Agent to identify suitable openings. The agent extracts only necessary candidate information, retrieves current Job Roles and Evaluation Criteria, explains its matches, avoids duplicates, and creates traceable hiring records for human review.

Use an intake-desk analogy: native file handling reads the packet, MCP checks the live vacancy register, and Dataverse records every accepted action.

Mission objectives

• Process supported resume uploads safely.
• Extract structured candidate and resume data.
• Query Dataverse job criteria through MCP.
• Match candidates using explicit, job-relevant criteria.
• Create and verify Candidate, Resume, and Job Application records.
• Evaluate extraction, matching, deduplication, and failure behavior.

Prerequisites to state in the mission

• Completion of Missions 01–04.
• Seeded Job Roles and Evaluation Criteria.
• Hiring Agent skills and Dataverse MCP connection.
• Supplied fictitious resume test files.
• Permission to create rows and upload files in Dataverse.

Proposed lab outline

Define the intake contract

Specify supported file types and sizes, required fields, extraction schema, missing-data behavior, privacy limits, and human confirmation before writes.

Extract and validate

Upload one fictitious resume, extract candidate name, contact details, experience, education, skills, and source evidence into a structured format. Ask for clarification rather than invent missing values.

Match against live criteria

List active Job Roles and associated Evaluation Criteria through Dataverse MCP. Score only documented job-relevant evidence, explain strengths and gaps, and present top matches for recruiter confirmation.

Create records

Check for an existing candidate by normalized email, create or update the Candidate, create the Resume record and file, and create the confirmed Job Application with correct lookups and status.

Evaluate edge cases

Test two distinct resumes, duplicate submission, unsupported file, missing email, poor scan, no suitable role, and attempted protected-characteristic matching.

Acceptance criteria

• Use native file handling rather than a classic AI Builder prompt.
• Validate supported formats, size limits, and model capabilities in the live environment.
• Provide a documented extraction schema and never fabricate missing fields.
• Query Job Roles and Evaluation Criteria at runtime through Dataverse MCP.
• Match only on explicit job-relevant criteria and show evidence and uncertainty.
• Require human confirmation before creating an application for a recommended role.
• Deduplicate candidates using a documented normalized key.
• Create Candidate, Resume, and Job Application records with valid lookups and the uploaded file.
• Verify records in Hiring Hub and include rollback guidance for test data.
• Add a versioned evaluation set covering happy paths and edge cases.
• Preserve <mission-meta /> and the Operative Mission 05 analytics tag.
• Follow repository writing and validation standards.

Out of scope

• OCR tuning for arbitrary low-quality documents.
• Automated hiring decisions or candidate rejection.
• Advanced machine-learning ranking.
• Email-triggered intake, which belongs in Mission 07.

Risks

• Native file support differs by model and file format.
• Resume data is sensitive and requires minimization and access control.
• MCP writes can create duplicate or partial records.
• Matching logic can encode bias if criteria are vague.
• Dataverse display and internal names can diverge.

References

• Current multimodal mission
• Current Dataverse grounding mission
• Dataverse connector reference
• File columns in Dataverse
• Microsoft Responsible AI