Explain how agents reason
Describe what makes a system an agent, and the reasoning patterns that let it plan and act.
Partners and press
Overview
Before you build an agent, learn its loop. This free exam checks that you have.
Oracle’s free entry-level agentic AI credential opens its new track, for people with some Python and basic LLM and OCI knowledge. Six modules cover agent basics, LangChain, MCP, OpenAI’s agent stack, enterprise agents on OCI and agents for Oracle AI Database. The exam is 40 questions in 60 minutes with a 65% passing score, and the credential stays valid for two years.
10Modules
40Questions
60Minutes
7Roles
What it tests
Learn all the way down.
Learn all the way down.
Perform when it matters.
Build with open frameworks
Create agents with LangChain and OpenAI’s Agents SDK, and know when a single API call is enough.
Connect tools with MCP
Use the Model Context Protocol to give an agent access to outside tools and data.
Place agents on Oracle
Describe how agents run on OCI Enterprise AI and inside Oracle AI Database.
Course outline
From a model that answers
From a model that answers
to an agent that acts.
We design this course ourselves. Oracle runs the exam and issues the credential, and LUNARTECH is independent of Oracle.
An agent is built on a model, so start there. You learn what tokens, context windows and sampling mean in practice, how a model calls a tool, and why structured output matters when software reads the reply.
- Tokens, context windows and cost
- Temperature and other sampling settings
- How a model is asked to call a tool
- Structured output and JSON schemas
- What a model cannot know
- Calling a model from Python
You build A small Python script that sends a prompt to a model and parses a structured reply.
Not every AI feature is an agent. You learn the parts of one, which are a goal, a model, tools and a loop, and how an agent differs from a single call or a fixed workflow.
- The agent loop: observe, decide, act
- Goals, tools, memory and state
- Agents versus workflows versus single calls
- Levels of autonomy
- Typical agent tasks and where agents fail
You build A one-page comparison that classifies five real tasks as call, workflow or agent.
Agents need a way to decide the next step. You study the common reasoning patterns, including reasoning with action, planning before acting and reflecting on results, and when each one earns its extra calls.
- Chain-of-thought reasoning
- ReAct: reasoning interleaved with action
- Plan first, then execute
- Reflection and self-critique
- Stopping conditions and step limits
- Cost and latency of each pattern
You build The same task solved with two reasoning patterns, with their results compared.
LangChain gives common parts a standard shape. You build an agent from models, prompts, tools and memory, follow its steps as it runs, and learn when a framework helps and when it only adds weight.
- Models, prompts and output parsers
- Defining tools an agent can call
- Building and running an agent
- Adding conversation memory
- Graph-based orchestration with LangGraph
- Reading intermediate steps when something goes wrong
You build A LangChain agent with two tools and conversation memory.
Every agent needs tools, and wiring each one by hand does not scale. MCP gives agents a standard way to discover and use tools and data, and you learn both sides of it, the server and the client.
- Why a standard protocol for tools
- Servers, clients and hosts
- Tools, resources and prompts
- Writing a small MCP server
- Connecting an agent to an MCP server
- Trust and permissions for connected tools
You build A small MCP server and an agent that uses its tools.
Choose the lightest tool that works. The Responses API suits single calls, while the Agents SDK adds multi-step logic, several agents, guardrails and tracing. You build with both and learn which a job needs.
- Single calls with the Responses API
- Giving tools to a Responses API call
- Agents, instructions and tools in the Agents SDK
- Handoffs between agents
- Guardrails on input and output
- Tracing a run
You build One task built twice, as a single API call and as an SDK agent, with the differences noted.
An agent that worked once may not work again. You build a small set of test tasks, trace runs to see why they fail, and decide where a person must approve what the agent does.
- Test tasks with expected outcomes
- Reading traces to find the failing step
- Common failures: loops, wrong tools, bad arguments
- Human approval for risky actions
- Limits on cost and steps
You build A test set and trace review for one of your agents.
Enterprises need agents that run under governance. You see how OCI Enterprise AI hosts agents with their tools and memory, and what changes when an agent moves from a laptop to a managed platform.
- What OCI Enterprise AI provides
- Hosted agents and how they run
- Attaching tools and memory
- Access control and governance for agents
- Observing and costing a hosted agent
You build A design sketch of your agent running as a hosted agent, with its tools, memory and access rules.
Much of an enterprise’s knowledge lives in its database. You meet Oracle AI Vector Search for finding data by meaning, Select AI Agent, Private Agent Factory and the Autonomous AI Database MCP server.
- Vector search: finding data by meaning
- Select AI Agent in outline
- Private Agent Factory in outline
- The Autonomous AI Database MCP server
- Choosing the right database-side option
- Keeping agents inside database permissions
You build A decision table that maps four database-agent needs to the right Oracle option.
Take one agent and follow it through its life. You build it with an open framework, expose its tools through MCP, and describe how it would run on OCI and use an Oracle AI Database.
- Choosing a task worth giving to an agent
- Building the agent and its tools
- Exposing a tool through MCP
- Adding checks and a human approval step
- Planning the move to OCI and the database
- Explaining the design to a colleague
You build A working agent with an MCP tool, tests and a deployment sketch for OCI.
Issued by
Certifying body · Independent of LUNARTECH
Oracle
Oracle runs this certification program itself, through Oracle University, with exams taken online or at test centers. It sorts its credentials into Foundations, Associate and Professional levels. LUNARTECH is independent of Oracle.
How you get there
Top-tier roles go to people who can do the work.
Top-tier roles go to people who can do the work.
Here’s how you get there.
Know where you start.
Oracle recommends basic familiarity with LLM concepts, functional Python and basic OCI knowledge. It also welcomes people new to agentic AI.
Work through the course.
Ten modules, designed by LUNARTECH, at your own pace.
Sit it with Oracle.
Oracle runs the exam and issues the credential, so you register and book directly with them.
Support
Support at every stage.
Support at every stage.
You won’t be working through this alone.
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All the way through
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After the last lesson
Stay in the network.
Your alumni network stays with you: ongoing support, opportunities and industry insight.
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Interview preparation
Preparation for the questions that decide it, so you walk in ready.
Network and search
Connections with industry leaders, and an alumni network that stays with you.
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Your certificate
It doesn’t raise its voice. Neither will you.
Finish a LUNARTECH course and its certificate is issued in your name, under our signature. Oracle’s credential comes from Oracle, when you pass its exam.
Certificate of completion
Your Name
Agentic AI Foundations course
LUNARTECH RISE Academy
Pricing
This course comes with the membership.
This course comes with the membership.
So does everything else.
One membership opens every course, certification, workshop and masterclass in the Academy. Every plan starts with a 3-day free trial. You register and book the exam directly with Oracle.
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Included in every plan
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- A 3-day free trial
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In practice
Seven roles.
Seven roles.
One credential across them all.
No single job owns it. Here’s where each role leans hardest, and where its work ends up.
- 01Fundamentals
- 02LangChain
- 03MCP
- 04OpenAI stack
- 05OCI Enterprise AI
- 06AI Database
-
Frameworks come and go; the agent loop stays. The first modules teach that loop.
Fundamentals, LangChain, MCP
Oracle Cloud Infrastructure, Software teams
-
A roadmap needs the same words engineers use. Fundamentals supply them.
Fundamentals, OCI Enterprise AI
Enterprise applications, Oracle Cloud Infrastructure
-
Choosing a framework is a design call. LangChain and OpenAI’s stack each get a module.
LangChain, OpenAI stack, AI Database
Software teams, Oracle databases
-
Agents for Oracle AI Database sit close to your tables. The last module shows how they connect.
AI Database
Oracle databases
-
MCP and OCI Enterprise AI are two ways to wire agents in. Pick by the system you must reach.
MCP, OCI Enterprise AI
Oracle Cloud Infrastructure, Enterprise applications
-
A feature roadmap needs a feel for what agents can do. The fundamentals module supplies it.
Fundamentals
Software teams
-
LLM apps are built in frameworks like LangChain. See how an agent chains steps and tools.
Fundamentals, LangChain
Software teams
- Oracle Cloud Infrastructure
- Oracle databases
- Enterprise applications
- Software teams
AI Engineer
Frameworks come and go; the agent loop stays. The first modules teach that loop.
Over 10,000 students trust LUNARTECH
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The field
Anyone can use AI.
Anyone can use AI.
Few can engineer it. This is where that starts.
+340%growth in open roles requiring AI skills
Roles and pay
~35,000 open roles right now
Open roles: Built In, Greenhouse, LinkedIn.
Typical salaries: Glassdoor, Levels.fyi, Payscale, Indeed, Ravio, JobRise, U.S. BLS. Military: 2026 U.S. basic pay, drone officers with aviation incentive pay.
Updated November 2026.
FAQ
Frequently asked questions.
Need help? These answers cover most of what people ask before they start.
No. Oracle issues it. LUNARTECH is independent of Oracle, and its names and marks belong to it. When you finish a course of ours, you earn a LUNARTECH certificate of completion for that course.
No. We design this course ourselves, around what the exam covers. Oracle has no part in it, and you still register and book the exam directly with Oracle.
40 questions, 60 minutes, online. The passing score is 65%.
Oracle recommends basic familiarity with LLM concepts, functional Python and basic OCI knowledge. It also welcomes people new to agentic AI.
2 years.
No. Oracle says its learning path includes a free Foundations Associate certification exam.
We last reviewed it on 5 November 2026, from excerpts of Oracle’s official pages, because its site doesn’t accept automated readers. Details change, so check the official page before you book.
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The course
Build the skills behind Agentic AI Foundations.
Show you understand how agents work, and how to build them. Start with a free trial, or enroll in a plan and work through the course at your own pace. Oracle runs the exam and issues the credential.
LUNARTECH is independent of the vendors and certifying bodies listed here. Their names, exam titles and marks belong to them, exams are run by them, and credentials are issued by them. Reviewed 5 November 2026. Catalogues change often, so confirm the details on the official page before you book.
