Build production agentic applications on OCI using Oracle Agent Development Kit with multi-agent orchestration, function tools, and enterprise patterns
SkillScore75Maintained
Automatic
◆AI Engineer pack
Last verified 9/2/2026
What it changes
Passed 18 of 24 test cases with the skill, and 7 of 24 without it.
The workflow is constructed using the Workflow class.
and 14 more in the full report
12 fixed6 passed either way5 still failing1 broke
Pass-rate lift
+46 pp
Tokens
+69%
Agent turns
0%
Response time
-10%
Model gemini-3.6-flashMeasured 9/2/2026v118 of 24 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 12
Prompt
We want to construct a customer support agent on OCI using a no-code visual flow builder interface where business analysts can drag and drop LLM nodes. Which Oracle framework should we adopt for this visual interface requirement?
case-01 · gemini-3.6-flash · 9/2/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md384 linessingle file
Oracle ADK Expert Skill
Purpose
Master Oracle's Agent Development Kit (ADK) for building enterprise-grade agentic applications on OCI Generative AI Agents Service with code-first approach and advanced orchestration patterns.
Platform Overview
OCI Agent Development Kit (Released May 22, 2025)
Client-side library that simplifies building agentic applications on top of OCI Generative AI Agents Service.
Key Value: Code-first approach for embedding agents in applications (web apps, Slackbots, enterprise systems).
Requirements: Python 3.10 or later
Core Capabilities
1. Multi-Turn Conversations
Build agents that maintain context across multiple interactions.
Pattern:
from oci_adk import Agent
agent = Agent(
name="customer_support",
model="cohere.command-r-plus",
system_prompt="You are a helpful customer support agent"
)
# Multi-turn conversation
conversation = agent.create_conversation()
response1 = conversation.send("I need help with my order")
response2 = conversation.send("It's order #12345")
# Agent remembers context from previous messages