oracle-agent-spec-expert

name	Oracle Agent Spec Expert
description	Design framework-agnostic AI agents using Oracle's Open Agent Specification for portable, interoperable agentic systems with JSON/YAML definitions
version	1.1.0
last_updated	"2026-01-06T00:00:00.000Z"
external_version	Agent Spec 1.0

Oracle Agent Spec Expert Skill

Purpose

Master Oracle's Open Agent Specification (Agent Spec) to design framework-agnostic, declarative AI agents that can be authored once and deployed across multiple frameworks and runtimes.

What is Agent Spec?

Open Agent Specification

Framework-agnostic declarative language for defining agentic systems, building blocks for standalone agents and structured workflows, plus composition patterns for multi-agent systems.

Key Innovation: Decouple design from execution - write agents once, run anywhere.

Release: Technical report published October 2025 (arXiv:2510.04173)

Core Philosophy

The Problem: Fragmented agent development - each framework requires different implementation.

The Solution: Unified representation - Agent Spec defines structure and behavior in JSON/YAML that any compatible runtime can execute.

Benefit: Author agents once → Deploy across frameworks → Reduce redundant development.

Architecture

Component Model

Agent Spec defines conceptual building blocks (components) that make up agent-based systems.

Key Property: All components are trivially serializable to JSON/YAML.

Core Components

1. LLMNode

Purpose: Text generation via LLM

Definition:

type: LLMNode
name: "text_generator"
model: "claude-sonnet-4-5"
system_prompt: "You are a helpful assistant"
temperature: 0.7
max_tokens: 2000

2. APINode

Purpose: External API calls

Definition:

type: APINode
name: "weather_api"
endpoint: "https://api.weather.com/v1/current"
method: "GET"
parameters:
  location: "{input.location}"
headers:
  Authorization: "Bearer {env.API_KEY}"

3. AgentNode

Purpose: Multi-round conversational agent

Definition:

type: AgentNode
name: "support_agent"
model: "gpt-4"
system_prompt: "You are a customer support specialist"
tools:
  - type: function
    name: "lookup_order"
  - type: function
    name: "process_refund"

4. WorkflowNode

Purpose: Orchestrate sequence of nodes

Definition:

type: WorkflowNode
name: "data_pipeline"
steps:
  - node: extract_node
  - node: transform_node
  - node: load_node
error_handling: retry

Agent Specification Format

Basic Agent

{
  "version": "1.0",
  "agent": {
    "name": "CustomerSupportAgent",
    "description": "Handles customer inquiries and support requests",
    "components": {
      "classifier": {
        "type": "LLMNode",
        "model": "claude-haiku-4",
        "system_prompt": "Classify customer inquiry type",
        "output": "inquiry_type"
      },
      "technical_support": {
        "type": "AgentNode",
        "model": "claude-sonnet-4-5",
        "tools": ["diagnose_issue", "escalate_ticket"]
      },
      "billing_support": {
        "type": "AgentNode",
        "model": "gpt-4",
        "tools": ["lookup_invoice", "process_refund"]
      },
      "router": {
        "type": "ConditionalNode",
        "conditions": [
          {
            "if": "inquiry_type == 'technical'",
            "then": "technical_support"
          },
          {
            "if": "inquiry_type == 'billing'",
            "then": "billing_support"
          }
        ]
      }
    },
    "entry_point": "classifier"
  }
}

Multi-Agent System

version: "1.0"
system:
  name: "ResearchSystem"
  description: "Multi-agent research and analysis system"

  agents:
    researcher:
      type: AgentNode
      model: claude-sonnet-4-5
      tools:
        - web_search
        - fetch_document
      system_prompt: "Research topics thoroughly"

    analyzer:
      type: AgentNode
      model: gpt-4o
      tools:
        - analyze_data
        - generate_insights
      system_prompt: "Analyze research findings"

    synthesizer:
      type: AgentNode
      model: claude-sonnet