OOSDK: YAML Ontologies Orchestrate AI Agents Like Palantir

Multi-Agent Limitations: AI Lacks Business Context

Current multi-agent systems succeed on simple tasks like "check my email" but fail complex business processes. In a tested 6-agent MCP (Email, CRM, Calendar, CS, Helpdesk, Report) on GCP with orchestrators (Claude, GPT-4o, Llama 3.1) achieving 100% success and Google ADK integration, the core issue persists: orchestrator AI selects agents and execution AI picks tools from zero context each time. AI ignores VIP customer status, complaint routing to CS, or ticket-closure surveys. Solution requires pre-injecting business knowledge—object types (e.g., Customer, Lead, Ticket), link types (Customer generates Lead), and action rules with governance.

Palantir Foundry excels here: ontology as operational layer where LLMs query structured objects/relationships/actions before decisions. Writes enforce validation/approvals/audits. Ontology orchestrates agents, reversing the flow for enterprise reliability without Palantir costs.

Three orchestration approaches compared:

• Autonomous (Claude/Cursor+GPT/Llama/Google ADK): Flexible PoC, no guardrails.
• Developer Manual (LangGraph): Predictable/auditable, but code changes for every process tweak.
• Ontology (OOSDK): YAML rules interpreted by AI—autonomous flexibility + manual auditability; non-devs edit YAML to alter behavior instantly, no deployments.

OOSDK Components: Static Rules, Engine Processing, Tiered Memory

OOSDK bridges clients and agents, injecting context pre-decision.

1. ontology.yaml: Declares business world in three sections—no Python needed.

   object_types:
     Customer:
       fields: [name, email, tier]
       source: salesforce
     Lead:
       fields: [email, status, score]
       source: crm
     Ticket:
       fields: [subject, priority]
       source: helpdesk
   links:
     Customer → Lead: type: generates
     Lead → Opportunity: type: converts_to
   rules:
     auto_approve:
       if: lead.score > 80
       then: approve
     escalate_vip:
       if: customer.tier == "VIP"
       then: priority = "high"
     route_support:
       if: email.type == "complaint"
       then: agent = "cs_agent"
   

   
   Business analysts tweak thresholds (e.g., VIP from tier to spend_amount > 10000) for immediate effect.
2. OntologyEngine: Loads YAML, processes triggers via four methods:

   class OntologyEngine:
       def __init__(self, yaml_path):
           self.ontology = load_yaml(yaml_path)
           self.memory = ThreeTierMemory()
       def resolve_context(self, trigger: dict):
           links = self.resolve_links(trigger)  # e.g., email → customer leads/tickets
           rules = self.check_rules(trigger, links)  # deterministic: score>80 auto-approves
           memory = self.manage_memory(trigger)  # tiered recall
           return {"links": links, "applied_rules": rules, "memory": memory, "suggested_agent": rules.get("route_to")}
   

   • resolve_links(): Fetches related objects.
   • check_rules(): Applies non-AI business logic.
   • trigger_events(): Post-action chains (e.g., ticket close → survey + lead update + follow-up).
   • manage_memory(): Handles 3-tier store.
3. 3-Tier Memory: Bounded runtime state:
   • Hot: Real-time session, cleared post-interaction.
   • Warm: Last 30 days (interactions/tickets/emails).
   • Cold: Lifetime summaries (spend/trends). Engine selects tiers per rules, keeping context windows manageable even for 100k+ customers.

Transformed Flow: Context-First Decisions and Event Chains

Before: request → Orchestrator (picks agent) → Execution (picks tool) → Execute.

After: request → OntologyEngine (resolve_links/check_rules/manage_memory) → Orchestrator (with context) → Execution → Execute → OntologyEngine (trigger_events).

AI decisions narrow dramatically; actions propagate (1 action → 3 events). Roadmap tests in scenarios:

• Customer pipeline: Lead scoring/routing/SLA (5-6 objects, SFDC).
• Customer 360°: Email response with/without ontology; memory demo.
• Order-to-Cash: Quote→Order→Delivery→Invoice→Payment (SFDC+Odoo, multi-agent across silos).

Shifts focus from smarter AI to surrounding business logic—YAML edits by sales/CS managers enable agile processes without devs.