==================================================
 STORYGEN AI — STORY EXPORT
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Story ID:           22
Title:              TEST_ERP_AP
Owner:              Fauziah Eka Damayanti - Full Stack Developer <fauziah.eka@educlaas.com>
App Type:           Agentic AI App
Input Type:         text
Status:             features_generated
Created:            2026-04-29 06:44:14 UTC
Updated:            2026-04-30 02:16:06 UTC
Features Generated: 2026-04-30 02:16:06 UTC
Total Clusters:     7
Total Features:     18

--- ORIGINAL INPUT ---
Currently, the procurement process in our SaaS company focuses on acquiring software tools, cloud infrastructure, third-party APIs, and service vendors. The process is still handled manually.

The teams involved include engineers (who request tools or APIs), finance, procurement, and managers for approvals.

Current workflow:
- Engineers or teams submit requests for tools, API subscriptions, or external services
- Approvals are handled manually and lack a structured workflow
- Finance or procurement completes purchases or subscribes to services
- Tracking of usage, costs, and subscription renewals is not centralized

Key problems:
- Lack of visibility into active SaaS subscriptions and overall spending
- Frequent duplication of tools across different teams
- Missed subscription renewals
- Difficulty tracking ROI for tools and vendors
- Slow and poorly documented approval processes

We aim to build a procurement ERP system tailored for a SaaS environment that manages the full lifecycle of procurement—from request submission and approval workflows to vendor and subscription management, as well as real-time cost and usage tracking.

The system is also expected to include AI capabilities to:
- Recommend the best tools or vendors based on historical usage
- Detect potential duplication of tools across teams
- Provide alerts for upcoming subscription renewals
- Analyze cost efficiency and ROI for each procurement decision

--- USER STORY ---
# AI-Driven Procurement ERP for SaaS Operations

## Overview
This is an **Agentic AI App** that reimagines procurement for a SaaS company by embedding autonomous AI agents across the full procurement lifecycle — from request intake and vendor recommendation to approval routing, subscription management, and ROI analysis. Instead of manual ticketing and disconnected spreadsheets, intelligent agents perceive procurement signals (requests, usage data, contracts, spend), reason over historical patterns, and act by orchestrating approvals, flagging duplication, predicting renewals, and surfacing cost-optimization decisions to humans.

## Actors
- **Engineer / Requester**: Submits procurement requests for tools, APIs, cloud services, or vendors via natural language or structured form.
- **Engineering Manager / Department Head**: Reviews agent-prepared approval packets and approves or rejects requests within their authority.
- **Finance Officer**: Validates budget impact, oversees high-value approvals, and reviews ROI dashboards.
- **Procurement Officer**: Manages vendor relationships, contracts, and final purchase execution; handles edge cases escalated by agents.
- **System Administrator**: Configures policies, spend thresholds, agent guardrails, and integrations.
- **Intake Agent**: Parses incoming requests, normalizes them, enriches with vendor metadata, and creates a structured procurement ticket.
- **Recommendation Agent**: Analyzes historical usage and existing contracts to suggest the best-fit tool/vendor or recommend reusing an existing subscription.
- **Duplication Detection Agent**: Continuously scans active subscriptions and pending requests to identify overlapping or redundant tools across teams.
- **Approval Routing Agent**: Determines the correct approval chain based on cost, category, and policy, and orchestrates the workflow.
- **Renewal & Lifecycle Agent**: Monitors subscription end dates, license utilization, and contract terms; issues proactive renewal/cancellation recommendations.
- **ROI & Cost Analytics Agent**: Correlates spend with usage telemetry to score each tool's ROI and flag underutilized or overpriced subscriptions.

## Goals
- Provide a single source of truth for all SaaS procurement, subscriptions, and spend.
- Reduce procurement cycle time through AI-driven intake, recommendation, and approval routing.
- Eliminate duplicate tool purchases across teams.
- Prevent missed renewals and uncontrolled auto-renewals.
- Maximize ROI by continuously analyzing tool usage versus cost.
- Maintain auditable, policy-compliant approval trails.

## User Story
As an **engineering team lead**, I want to submit a procurement request and have AI agents autonomously evaluate, recommend, route, and track it through the full lifecycle, so that my team gets the right tools quickly while finance maintains visibility, control, and confidence in spend efficiency.

## Detailed Workflow

1. **Request Intake (Intake Agent)**
   - An engineer submits a request ("We need an APM tool for our backend services, ~$500/month") via web form, Slack, or email.
   - The Intake Agent extracts entities (category, budget, team, urgency, technical requirements), enriches with vendor catalog data, and creates a structured procurement record.
   - If the request is ambiguous or missing critical fields (e.g., budget, business justification), the agent asks the requester clarifying questions before proceeding.

2. **Recommendation & Duplication Check (Recommendation Agent + Duplication Detection Agent)**
   - The Recommendation Agent queries the internal subscription registry and historical usage data to suggest top candidate vendors, including pricing tiers, prior team experience, and contract terms.
   - In parallel, the Duplication Detection Agent checks whether another team already has a license for an equivalent tool with available seats or capacity.
   - If duplication is detected, the agent proposes reuse (e.g., "Team B has 5 unused Datadog seats — reassign instead of buying new") and routes the request to the existing license owner for confirmation.

3. **Approval Routing (Approval Routing Agent)**
   - Based on cost thresholds, category policies, and budget owner mapping, the agent constructs the approval chain (e.g., Manager → Finance → Procurement for >$10K).
   - It assembles an "approval packet" containing the request, recommendation rationale, duplication findings, ROI projection, and policy compliance check.
   - Approvers receive notifications with one-click approve/reject and can ask the agent follow-up questions.
   - The agent escalates to a human procurement officer if approvers are unresponsive beyond a configurable SLA.

4. **Procurement Execution (Procurement Officer + Intake Agent)**
   - Upon full approval, the Procurement Officer executes the purchase or initiates the vendor contract.
   - The Intake Agent records contract terms, billing cadence, license counts, owner, and renewal date in the subscription registry.

5. **Ongoing Monitoring (Renewal & Lifecycle Agent + ROI & Cost Analytics Agent)**
   - The Renewal Agent tracks all upcoming renewals and issues alerts at 90/60/30 days, accompanied by a renewal recommendation (renew, renegotiate, downgrade, cancel) based on usage data.
   - The ROI Agent continuously scores each subscription, flagging underused tools and producing monthly cost-efficiency reports for Finance.
   - The Duplication Agent runs scheduled scans to surface newly emerging redundancies as the tool portfolio evolves.

6. **Human Hand-offs**
   - All agent recommendations affecting spend, contracts, or cancellation require human approval before execution.
   - Agents log every decision, data source consulted, and rationale to a transparent audit trail.

## Acceptance Criteria

**Functional**
- Users can submit procurement requests through web form, Slack, and email; all are normalized into a single structured record.
- The system maintains a centralized registry of every active subscription, contract, owner, cost, and renewal date.
- Approval chains dynamically adjust based on configurable policies (cost, category, department).
- Renewal alerts are generated at 90, 60, and 30 days prior to contract end with at least 99% reliability.
- Duplicate tool detection runs on every new request and on a scheduled cadence across the portfolio.
- ROI dashboards display per-tool cost, usage metrics, and an efficiency score, refreshed at least daily.

**Agent-Specific Guardrails**
- No agent may execute a purchase, cancellation, contract signature, or budget commitment without explicit human approval.
- The Recommendation Agent must cite the data sources (historical contracts, usage logs, vendor catalog) behind every suggestion.
- If the Intake Agent's confidence in parsing the request is below a configured threshold, it must defer to the human requester for clarification rather than guess.
- The Duplication Detection Agent must present evidence (subscription IDs, owners, utilization %) when claiming a duplicate; ambiguous matches are flagged for human review, not auto-merged.
- The Approval Routing Agent must escalate to a human procurement officer if (a) policy ambiguity exists, (b) approvers are unresponsive past SLA, or (c) the request exceeds a top-tier spend threshold.
- The Renewal Agent must not trigger silent auto-renewals; all renewal actions require human confirmation.
- All agents must log every decision, input, and action to an immutable audit trail accessible to administrators.
- Stop condition: any agent encountering data integrity errors, missing budget owner, or conflicting policies must halt its workflow and notify the System Administrator.

**Non-Functional**
- End-to-end procurement cycle time (request to approval) reduced by at least 50% compared to baseline.
- 95% of subscription renewals are acted upon before expiry.
- Role-based access control governs visibility into spend, contracts, and approvals.

## Assumptions & Constraints
- Integrations exist or can be built for SSO, Slack/email, accounting systems (e.g., NetSuite/QuickBooks), and major SaaS vendor APIs for usage telemetry.
- Historical procurement and usage data is available in sufficient volume to train/seed the Recommendation and ROI agents; cold-start behavior must rely on vendor catalog defaults.
- The company will define and maintain procurement policies (spend thresholds, approval matrices, preferred vendors) that the agents can consume as configuration.
- All agent actions are auditable and comply with the company's internal financial controls and SOC 2 requirements.
- Initial scope covers SaaS tools, APIs, cloud infrastructure, and service vendors; hardware procurement is out of scope for v1.
- Human approvers remain accountable for all purchase decisions; agents are advisory and orchestrating, not autonomous spenders.

--- FEATURE LIST SUMMARY ---
This solution enables a SaaS company to run procurement end-to-end through autonomous AI agents that intake requests, recommend vendors, detect duplication, route approvals, manage subscriptions, and analyze ROI. Primary actors include Engineers, Engineering Managers, Finance Officers, Procurement Officers, System Administrators, and a fleet of specialized AI agents. The flow moves from policy and master data setup, through natural-language request intake and recommendation, into duplication detection, dynamic approval routing, procurement execution, and ongoing renewal and ROI monitoring. Master Data Configuration holds users, roles, departments, vendor catalog, policies, spend thresholds, and agent guardrails. Total of 18 rows delivering AI-driven intake, duplicate elimination, and proactive renewal/ROI optimization.

Feature Clusters & Features:
• Master Data Configuration
  - 1. User and Role Management — Central registry of user accounts, roles, and RBAC permissions used across procurement workflows and approvals.
  - 2. Department and Budget Owner Directory — Maintains organizational units, budget owners, and reporting hierarchies referenced by approval routing.
  - 3. Vendor Catalog — Master list of known SaaS vendors, categories, pricing tiers, and metadata used for enrichment and recommendations.
  - 4. Procurement Policy and Threshold Configuration — Stores spend thresholds, category policies, and approval matrices that drive dynamic routing.
  - 5. Agent Guardrail and Configuration Registry — Defines confidence thresholds, SLA timers, stop conditions, and tool permissions for every AI agent.
• Request Intake
  - 6. Multi-Channel Request Submission — Lets engineers submit procurement requests via web form, Slack, or email into one normalized pipeline.
  - 7. AI Request Parsing and Enrichment — Intake Agent extracts entities, enriches with vendor data, and creates a structured procurement record.
  - 8. Clarification Dialogue — When parsing confidence is low or fields are missing, the agent asks the requester targeted clarifying questions.
• Recommendation and Duplication
  - 9. Vendor Recommendation Engine — Recommendation Agent suggests best-fit vendors with cited rationale from contracts, usage logs, and catalog.
  - 10. Duplicate Subscription Detection — Duplication Agent scans active subscriptions to flag overlapping tools and propose seat reuse.
  - 11. License Reuse Coordination — Routes reuse proposals to existing license owners for confirmation before any new purchase proceeds.
• Approval Workflow
  - 12. Dynamic Approval Chain Construction — Approval Routing Agent builds the correct approver sequence from cost, category, and policy rules.
  - 13. Approval Packet Assembly and Notifications — Bundles request, rationale, ROI projection, and compliance check for one-click approve/reject.
  - 14. SLA Escalation and Human Handoff — Escalates unresponsive approvals or policy ambiguity to a human procurement officer per guardrails.
• Procurement Execution
  - 15. Purchase Execution and Contract Capture — Procurement Officer finalizes purchase and the agent records contract terms and license details.
• Subscription Lifecycle Monitoring
  - 16. Renewal Tracking and Recommendations — Renewal Agent issues 90/60/30-day alerts with renew, renegotiate, downgrade, or cancel guidance.
  - 17. ROI and Cost Analytics Dashboard — ROI Agent correlates spend with usage telemetry and surfaces efficiency scores and underuse flags.
• Agent Orchestration and Audit
  - 18. Agent Decision Audit Trail — Immutable log of every agent input, data source, decision, and human action available to administrators.

--- FEATURE LIST (18 features across 7 clusters) ---

#1 | Cluster: Master Data Configuration | Feature: User and Role Management
  Description: Central authority for user accounts, role assignments, and RBAC permissions. Governs visibility into spend, contracts, approvals, and agent administration.
  Workflow:
    1. Administrator creates user accounts and assigns roles
    2. Role permissions are mapped to procurement actions
    3. SSO integration syncs identity claims
    4. Access reviews are performed periodically
    5. Deactivated users are archived with audit retention
  Table:       proc_users
  Columns:     id (bigint, pk), email (varchar 255), full_name (varchar 200), role (varchar 50), department_id (bigint, fk), sso_subject (varchar 255), is_active (boolean), created_at (timestamp)
  Actor:       System Administrator
  AI Agent:    None
  ----
#2 | Cluster: Master Data Configuration | Feature: Department and Budget Owner Directory
  Description: Maintains the org structure, cost centers, and budget owners used by the Approval Routing Agent. Ensures every request resolves to a responsible approver.
  Workflow:
    1. Admin defines departments and cost centers
    2. Budget owners are mapped to each unit
    3. Reporting hierarchies are configured
    4. Approval routing consumes this map
    5. Changes are version-tracked
  Table:       proc_departments
  Columns:     id (bigint, pk), name (varchar 150), parent_id (bigint, fk), budget_owner_user_id (bigint, fk), cost_center_code (varchar 50), annual_budget (decimal 14,2), is_active (boolean)
  Actor:       System Administrator
  AI Agent:    None
  ----
#3 | Cluster: Master Data Configuration | Feature: Vendor Catalog
  Description: Master list of known SaaS vendors with category, pricing, and contract metadata. Acts as the canonical reference for enrichment and recommendation.
  Workflow:
    1. Admin imports vendor master data
    2. Categories and pricing tiers are tagged
    3. Preferred-vendor flags are set
    4. Recommendation Agent enriches requests from catalog
    5. Catalog is refreshed on a schedule
  Table:       proc_vendors
  Columns:     id (bigint, pk), name (varchar 200), category (varchar 100), website (varchar 255), default_pricing_tier (varchar 50), is_preferred (boolean), risk_rating (varchar 20), created_at (timestamp)
  Actor:       Procurement Officer
  AI Agent:    Recommendation Agent
  ----
#4 | Cluster: Master Data Configuration | Feature: Procurement Policy and Threshold Configuration
  Description: Configurable rules for spend thresholds, category policies, and approval matrices. Drives dynamic, policy-compliant routing of every procurement request.
  Workflow:
    1. Admin defines spend thresholds per category
    2. Approval matrices are mapped to thresholds
    3. Policy rules are versioned
    4. Approval Routing Agent reads active policies
    5. Policy changes trigger guardrail reload
  Table:       proc_policies
  Columns:     id (bigint, pk), name (varchar 150), category (varchar 100), min_amount (decimal 14,2), max_amount (decimal 14,2), approval_chain_json (json), version (int), effective_from (date), is_active (boolean)
  Actor:       System Administrator
  AI Agent:    Approval Routing Agent
  ----
#5 | Cluster: Master Data Configuration | Feature: Agent Guardrail and Configuration Registry
  Description: Single registry of agent identities, guardrails, confidence thresholds, SLA timers, and stop conditions. Ensures every agent operates within sanctioned boundaries.
  Workflow:
    1. Admin defines confidence thresholds per agent
    2. SLA timers and escalation rules are set
    3. Stop conditions and tool scopes are registered
    4. Agents read config at runtime
    5. Changes are audit-logged
  Table:       proc_agent_configs
  Columns:     id (bigint, pk), agent_name (varchar 100), confidence_threshold (decimal 4,3), sla_minutes (int), allowed_tools_json (json), stop_conditions_json (json), version (int), updated_at (timestamp)
  Actor:       System Administrator
  AI Agent:    All Agents
  ----
#6 | Cluster: Request Intake | Feature: Multi-Channel Request Submission
  Description: Accepts procurement requests through multiple channels and normalizes them into a single structured pipeline. Removes friction for requesters while preserving traceability.
  Workflow:
    1. Engineer submits via web form, Slack, or email
    2. Channel adapter normalizes payload
    3. Request is queued for parsing
    4. Submitter receives acknowledgement
    5. Channel metadata is preserved
  Table:       proc_request_intakes
  Columns:     id (bigint, pk), submitter_user_id (bigint, fk), channel (varchar 30), raw_payload (text), received_at (timestamp), status (varchar 30), external_message_id (varchar 200)
  Actor:       Engineer / Requester
  AI Agent:    Intake Agent
  ----
#7 | Cluster: Request Intake | Feature: AI Request Parsing and Enrichment
  Description: Intake Agent uses NLP to extract entities and enrich requests with vendor catalog data. Produces a clean, structured procurement record ready for downstream agents.
  Workflow:
    1. Intake Agent extracts category, budget, urgency
    2. Vendor catalog is queried for enrichment
    3. Structured procurement record is created
    4. Confidence score is logged
    5. Record is forwarded to recommendation stage
  Table:       proc_requests
  Columns:     id (bigint, pk), intake_id (bigint, fk), requester_id (bigint, fk), department_id (bigint, fk), category (varchar 100), budget_amount (decimal 14,2), urgency (varchar 20), justification (text), parse_confidence (decimal 4,3), status (varchar 30), created_at (timestamp)
  Actor:       Intake Agent
  AI Agent:    Intake Agent
  ----
#8 | Cluster: Request Intake | Feature: Clarification Dialogue
  Description: When parsing confidence falls below threshold, the Intake Agent asks the requester clarifying questions instead of guessing. Enforces the human-in-the-loop guardrail.
  Workflow:
    1. Agent detects low confidence or missing fields
    2. Targeted clarifying questions are generated
    3. Requester responds via original channel
    4. Answers are merged into the request
    5. Request advances when complete
  Table:       proc_clarifications
  Columns:     id (bigint, pk), request_id (bigint, fk), question (text), answer (text), asked_at (timestamp), answered_at (timestamp), agent_name (varchar 100)
  Actor:       Intake Agent
  AI Agent:    Intake Agent
  ----
#9 | Cluster: Recommendation and Duplication | Feature: Vendor Recommendation Engine
  Description: Recommendation Agent suggests best-fit vendors with cited evidence from historical contracts, usage logs, and the vendor catalog. Accelerates informed decisions.
  Workflow:
    1. Agent queries subscription registry and usage data
    2. Top candidate vendors are scored
    3. Pricing tiers and prior experience are attached
    4. Cited data sources are recorded
    5. Recommendations are attached to the request
  Table:       proc_recommendations
  Columns:     id (bigint, pk), request_id (bigint, fk), vendor_id (bigint, fk), score (decimal 5,2), rationale (text), data_sources_json (json), created_at (timestamp)
  Actor:       Recommendation Agent
  AI Agent:    Recommendation Agent
  ----
#10 | Cluster: Recommendation and Duplication | Feature: Duplicate Subscription Detection
  Description: Duplication Agent surfaces overlapping subscriptions with evidence including subscription IDs, owners, and utilization. Prevents redundant tool purchases across teams.
  Workflow:
    1. Agent scans active subscriptions and pending requests
    2. Overlapping tools are matched by category and capability
    3. Available seats and utilization are computed
    4. Evidence packet is generated
    5. Ambiguous matches are flagged for human review
  Table:       proc_duplication_findings
  Columns:     id (bigint, pk), request_id (bigint, fk), existing_subscription_id (bigint, fk), match_confidence (decimal 4,3), available_seats (int), utilization_pct (decimal 5,2), evidence_json (json), status (varchar 30), created_at (timestamp)
  Actor:       Duplication Detection Agent
  AI Agent:    Duplication Detection Agent
  ----
#11 | Cluster: Recommendation and Duplication | Feature: License Reuse Coordination
  Description: Routes detected duplicates to the existing license owner for confirmation before any new purchase proceeds. Operationalizes seat reuse safely.
  Workflow:
    1. Reuse proposal is sent to existing license owner
    2. Owner confirms or declines reassignment
    3. Seats are reserved on confirmation
    4. Original request is closed or downsized
    5. Outcome is logged
  Table:       proc_reuse_proposals
  Columns:     id (bigint, pk), finding_id (bigint, fk), owner_user_id (bigint, fk), seats_offered (int), status (varchar 30), responded_at (timestamp), notes (text)
  Actor:       License Owner
  AI Agent:    Duplication Detection Agent
  ----
#12 | Cluster: Approval Workflow | Feature: Dynamic Approval Chain Construction
  Description: Approval Routing Agent builds the correct approver sequence dynamically from policies, budget owners, and request attributes. Removes manual routing decisions.
  Workflow:
    1. Agent reads active policies and budget owner map
    2. Approval chain is constructed by cost and category
    3. Approvers are notified in sequence
    4. Chain is persisted with the request
    5. Changes trigger re-routing
  Table:       proc_approval_chains
  Columns:     id (bigint, pk), request_id (bigint, fk), policy_id (bigint, fk), steps_json (json), current_step (int), status (varchar 30), created_at (timestamp)
  Actor:       Approval Routing Agent
  AI Agent:    Approval Routing Agent
  ----
#13 | Cluster: Approval Workflow | Feature: Approval Packet Assembly and Notifications
  Description: Bundles all relevant context into an approval packet and delivers it with one-click decisioning. Approvers can ask the agent follow-up questions inline.
  Workflow:
    1. Agent compiles request, recommendation, duplication, ROI
    2. Compliance check is appended
    3. Notification with one-click approve/reject is sent
    4. Approver questions are answered by the agent
    5. Decisions are recorded
  Table:       proc_approval_decisions
  Columns:     id (bigint, pk), chain_id (bigint, fk), step_no (int), approver_user_id (bigint, fk), decision (varchar 20), comments (text), packet_snapshot_json (json), decided_at (timestamp)
  Actor:       Engineering Manager / Finance Officer
  AI Agent:    Approval Routing Agent
  ----
#14 | Cluster: Approval Workflow | Feature: SLA Escalation and Human Handoff
  Description: Escalates unresponsive approvals, policy ambiguity, or top-tier spend to humans per guardrails. Ensures no request stalls or bypasses oversight.
  Workflow:
    1. SLA timers track approver response time
    2. Reminders are issued automatically
    3. Breach triggers escalation to procurement officer
    4. Policy ambiguity halts and notifies admin
    5. Top-tier spend always escalates
  Table:       proc_escalations
  Columns:     id (bigint, pk), chain_id (bigint, fk), reason (varchar 100), escalated_to_user_id (bigint, fk), sla_breached_at (timestamp), resolved_at (timestamp), status (varchar 30)
  Actor:       Procurement Officer
  AI Agent:    Approval Routing Agent
  ----
#15 | Cluster: Procurement Execution | Feature: Purchase Execution and Contract Capture
  Description: Procurement Officer finalizes the purchase and the Intake Agent records contract terms, licenses, owner, and renewal date. Establishes the subscription source of truth.
  Workflow:
    1. Procurement Officer executes purchase or contract
    2. Contract terms and billing cadence are captured
    3. License count and owner are recorded
    4. Renewal date is set
    5. Subscription registry is updated
  Table:       proc_subscriptions
  Columns:     id (bigint, pk), request_id (bigint, fk), vendor_id (bigint, fk), owner_user_id (bigint, fk), monthly_cost (decimal 14,2), seats_total (int), billing_cadence (varchar 30), start_date (date), renewal_date (date), contract_url (varchar 500), status (varchar 30)
  Actor:       Procurement Officer
  AI Agent:    Intake Agent
  ----
#16 | Cluster: Subscription Lifecycle Monitoring | Feature: Renewal Tracking and Recommendations
  Description: Renewal Agent issues proactive 90/60/30 day alerts with usage-grounded recommendations. Requires human confirmation before any renewal or cancellation action.
  Workflow:
    1. Agent monitors all renewal dates
    2. Alerts fire at 90, 60, 30 days
    3. Recommendation (renew/renegotiate/downgrade/cancel) is computed
    4. Owner confirms action
    5. No silent auto-renewals occur
  Table:       proc_renewal_alerts
  Columns:     id (bigint, pk), subscription_id (bigint, fk), alert_window_days (int), recommendation (varchar 30), rationale (text), status (varchar 30), confirmed_by_user_id (bigint, fk), created_at (timestamp)
  Actor:       Renewal & Lifecycle Agent
  AI Agent:    Renewal & Lifecycle Agent
  ----
#17 | Cluster: Subscription Lifecycle Monitoring | Feature: ROI and Cost Analytics Dashboard
  Description: ROI Agent correlates spend with usage telemetry to score each tool and surface underutilized or overpriced subscriptions. Drives continuous cost optimization.
  Workflow:
    1. Agent ingests usage telemetry per tool
    2. Cost-per-active-user metrics are computed
    3. Efficiency score is assigned
    4. Underused tools are flagged
    5. Monthly report is published to Finance
  Table:       proc_roi_scores
  Columns:     id (bigint, pk), subscription_id (bigint, fk), period_month (date), monthly_cost (decimal 14,2), active_users (int), utilization_pct (decimal 5,2), efficiency_score (decimal 5,2), flag (varchar 30), computed_at (timestamp)
  Actor:       Finance Officer
  AI Agent:    ROI & Cost Analytics Agent
  ----
#18 | Cluster: Agent Orchestration and Audit | Feature: Agent Decision Audit Trail
  Description: Immutable log of every agent decision, input, data source, and resulting human action. Underpins SOC 2 compliance and administrative oversight.
  Workflow:
    1. Every agent action emits a structured event
    2. Inputs, data sources, and rationale are captured
    3. Events are written to immutable storage
    4. Admins query trail by request or agent
    5. Stop-condition events trigger admin notifications
  Table:       proc_agent_audit_logs
  Columns:     id (bigint, pk), agent_name (varchar 100), request_id (bigint, fk), action (varchar 100), inputs_json (json), data_sources_json (json), rationale (text), outcome (varchar 50), created_at (timestamp)
  Actor:       System Administrator
  AI Agent:    All Agents
  ----

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