Agentic operating-model experiment
A multi-tenant business workflow prototype
A multi-tenant business-workflow prototype with departmental modules, queued agent execution, retrieval memory, and model routing. The source demonstrates orchestration boundaries, not autonomous business operation.
Business ↔ technology bridge · Agentic operating-model experiment
Explores tenant-scoped agents, organisational memory, queued execution, and human review boundaries.
This is a portfolio and source interpretation—not a customer, deployment, certification, or outcome claim.
Departmental workflows and knowledge are fragmented, while a chat interface can conceal who owns an action, which data it used, and whether work completed.
Scope data by tenant, queue agent work, separate model routing from business modules, and retain human review notices.
Web, API, worker, database, agent, model-routing, retrieval-memory, schema, and selected test packages.
Tool results, queue state, retrieval provenance, and review outcomes return to workflow design, permissions, staffing, and operating controls.
Source evidence
A multi-tenant business-workflow prototype with departmental modules, queued agent execution, retrieval memory, and model routing. The source demonstrates orchestration boundaries, not autonomous business operation.
Repository evidence is not client, deployment, certification, or outcome evidence.
Architecture
Three source boundaries: a business workspace, queued departmental agent workflows, and a retrieval-memory layer scoped by organisation.
Source map only: a layer can represent implemented code, an adapter boundary, or documented design. It does not assert a configured production service.
The source represents nine department modules under one Next.js shell and a multi-tenant design. It does not prove a certification, a customer integration, or the operational replacement of existing systems.
Modules expose specialised agent definitions for lead scoring, planning, triage, invoice review, recruitment support, and policy review. Provider and locality behaviour depend on configuration; no sovereignty guarantee is asserted.
Document chunking, vector embeddings, and retrieval are represented in source. Grounding quality depends on the ingested corpus, access controls, retrieval evaluation, and human review.
The Problem
Why business operations are broken
Separate CRM, marketing, support, finance, and HR systems that don't talk to each other. Teams waste hours copying data between platforms.
ChatGPT can write emails but doesn't know your sales pipeline, customer history, or company policies. Generic AI adds noise, not value.
Critical institutional knowledge lives in people's heads, scattered docs, and old emails. When someone leaves, the knowledge walks out the door.
GDPR, the AI Act, contracts, and sector rules can create different logging, oversight, security, and transfer obligations. Hosting geography alone neither establishes nor prevents compliance; roles, data flows, subprocessors, controls, and evidence all matter.
Modules
9 modules and 27 specialised agent definitions in source, 3 per department
Source scope only: an entry can describe implemented code, a documented adapter, or a configured workflow. It is not a statement that a production service is active.
01
Source representation: Lead scoring, deal tracking, and proposal generation. Agent definitions: LeadQualifier, ProposalWriter, DealCoach.
02
Source representation: Campaign management, content creation, and audience segmentation. Agent definitions: ContentStrategist, SeoWriter, CampaignManager.
03
Source representation: Ticket management, AI-assisted resolution, and escalation routing. Agent definitions: SupportAgent, EscalationRouter, VoiceOfCustomer.
04
Source representation: Invoice processing, expense tracking, and budget management. Agent definitions: InvoiceParser, Bookkeeper, FinancialAnalyst.
05
Source representation: Employee management, recruitment pipeline, and PTO workflows. Agent definitions: Recruiter, OnboardingGuide, HRPolicy.
06
Source representation: Document management with semantic search and Company Memory. Agent definitions: KnowledgeAssistant, MeetingScribe, ContractReviewer.
07
Source representation: Business reports, trend analysis, and custom dashboards. Agent definitions: BusinessAnalyst, CompetitiveIntel, TrendSpotter.
08
The source represents audit logs, compliance scanning, and regulatory checks. Agent definitions: ComplianceMonitor, GDPRAgent (Article-specific checks, data inventory, legal-basis drafts), LegalReviewer. It does not establish legal completeness or compliance.
09
Source representation: Workflow automation, task management, and process improvement. Agent definitions: WorkflowAutomator, OpsAnalyst, ProcurementAgent.
9 departments, one lead agent each
The source defines specialised agents for each department. Below: the lead agent definition for each module’s proposed day-to-day workflow.
Repository surface only: availability, completeness, performance, and operational fitness require separate validation.
SALESRepresents inbound and outbound lead scoring against a supplied ICP, next-best-action drafts, and stalled-pipeline flags. No CRM integration is established.
MARKETINGDrafts campaign briefs, channel options, copy, and KPI hypotheses from supplied context. Brand and performance claims require human review.
SUPPORTRepresents ticket classification by severity and topic, a suggested first response from a supplied knowledge base, and escalation routing to a human reviewer.
FINANCERepresents invoice extraction, purchase-order comparison, exception flags, and close-summary drafts. Financial authority and data locality remain external controls.
HRRepresents CV parsing against a job scorecard, candidate flags for interview, personalised outreach drafts, and funnel tracking. Screening decisions remain with a human reviewer.
KNOWLEDGERepresents ingestion, chunking, deduplication, vector retrieval, and cited answers for configured documents. Coverage is limited to authorised, successfully indexed material.
ANALYTICSRepresents anomaly checks on business KPIs, root-cause hypotheses against supplied data, and a draft executive narrative for the monthly review. Findings require human review.
COMPLIANCERepresents policy checks and draft evidence records for selected workflow outputs. It does not establish legal completeness or compliance.
OPSRepresents profiling of repeated multi-step workflows and suggestions for where AI agents could shorten them. Workflow changes and any time or cost estimate require human review.
Tech Stack
Named technologies identify repository dependencies or adapter surfaces; they do not assert a live integration or deployment.
Source Code
The Athena AI prototype source code is available upon request for evaluation and partnership purposes.
To access the source code, please sign the Non-Disclosure Agreement. This is a standard legal formality.
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Partnership
This prototype is for organisations exploring bounded cross-department workflows or a partnership around its source code.
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