Agentic Delivery Model (ADM)
The Agentic Delivery Model (ADM) is SkuzaAI’s proprietary five-layer architecture for designing, deploying, and scaling enterprise AI agents. It provides a repeatable, governed delivery model that allows organizations to move from a single production agent in weeks to a self-service agent-building capability within months. ADM is structured across five distinct layers – from a universal Data Fabric foundation up through an Intelligence Core, Action Engine, Governance Rail, and an Extension Hub that empowers client teams to build autonomously.
Layer 1
Extension Hub
Extensibility and self-service deployment layer. Standardised templates, governed scaffolding, and pre-approved patterns enabling autonomous capability expansion without central engineering involvement.
Layer 2
Governance Rail
Audit trails, human-in-the-loop controls, compliance monitoring, and role-based access. Ensures all agent outputs are logged and reviewable.
Layer 3
Action Engine
API integrations, workflow automation, tool execution, and external system connectors. This layer gives agents the ability to act on external systems.
Layer 4
Intelligence Core
LLM orchestration, prompt engineering patterns, multi-model routing, and context management. The reasoning and generation layer.
Layer 5
Data Fabric
Universal connectors to CRM, ERP, documents, email, databases, and web sources. The foundation that gives agents access to enterprise data.
Extension Hub
Purpose: Provide a governed extensibility layer that enables autonomous deployment of new capabilities using standardised templates and pre-approved architectural patterns.
Key Components:
- Pre-built deployment templates and configuration blueprints
- Scaffolding tools for component construction and integration mapping
- Validation and governance checklist — components must pass review before promotion to production
- Approved pattern and component library
- Role-based access controls aligned with Governance Rail
Governance Rail
Purpose: Provide immutable audit trails, human-in-the-loop controls, compliance enforcement, and role-based access management across all agents.
Key Components:
- Human-in-the-loop approval workflows for sensitive actions
- Compliance policy engine (configurable per client)
- Role-based access control (RBAC) management
- Data Fabric write-access approval gateway
Action Engine
Purpose: Execute actions on external systems by managing API integrations, workflow automation, and tool execution.
Key Components:
- Integration Registry (all connectors must be pre-registered)
- Workflow orchestration engine
- External API connector library
- Tool execution sandbox
- Retry and error-handling policies
Intelligence Core
Purpose: Orchestrate LLM calls, manage prompt engineering patterns, route across models, and maintain session context.
Key Components:
- Multi-model router (supports OpenAI, Anthropic, and client-approved models)
- Prompt engineering pattern library
- Context window management and summarization
- Response evaluation and self-critique loops
- PII scrubbing layer (context isolation)
Data Fabric
Purpose: Provide universal, governed read access to enterprise data sources including CRM, ERP, documents, email, databases, and web sources.
Key Components:
- CRM connector (Salesforce, HubSpot, and others)
- ERP connector (SAP, NetSuite)
- Document store connector (SharePoint, Google Drive, OneDrive)
- Email connector (Outlook, Gmail)
- Relational database connector (PostgreSQL, MySQL, SQL Server)
- Web data connector (REST APIs, web scraping)
SkuzaAI has developed and delivered four proven agent archetypes. Each maps to a set of enterprise use cases, engages specific framework layers, and has a recommended delivery phase. Archetypes can be combined within a single engagement to address cross-functional needs.
* Simple Single data source, single action, FAQ bot, single-field CRM update. Medium 2-3 integrations, multi-step workflow, lead scoring + CRM enrichment Complex – Multi-source, autonomous decision-making, full sales intelligence pipeline
ADM is delivered in three sequential phases. Phase 1 (ADOPT) focuses on quick time-to-value with the first production agent. Phase 2 (EXPAND) broadens coverage and builds client capability. Phase 3 (SCALE) transitions the client to self-sufficiency with SkuzaAI in a support role.
Phase 1: ADOPT | Weeks 1-6
Objective: Install the framework, establish integrations, deploy the first production agent, and onboard the client team.
Activities:
- Framework installation and configuration in client environment
- First production agent designed, built, and deployed
- System integrations established and validated
- Client team onboarding and initial training
Deliverables:
- Framework setup documentation
- Integration specifications
- Agent v1.0 in production
- Onboarding guide
Phase 2: EXPAND | Months 1-3
Objective: Build 2-3 additional agents, train client power users, and establish ROI measurement baselines.
Activities:
- 2-3 additional agents scoped, built, and deployed
- Power user training for client team
- ROI measurement dashboard configured and live
- Agent performance baseline established
Deliverables:
- 3-4 live production agents
- Power user playbook
- ROI dashboard
- Performance baseline report
Phase 3: SCALE | Month 3+
Objective: Transition client team to self-service agent building via the Extension Hub, activate the annual license, and shift to a support retainer model.
Activities:
- Client team builds own agents via Extension Hub
- SkuzaAI support retainer activated
- Framework license agreement executed
- Quarterly strategy review cadence initiated
Deliverables:
- Extension Hub training completion
- Signed license agreement
- Quarterly review cadence