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Foundations of AI Coding Agents with Google Antigravity
Run Google Antigravity as a multi-agent AI coding system rather than an autocomplete tool. AI coding agent architecture covers context windows, token limits, the plan-execute-iterate cycle,...
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$1,665USD
Duration
2 days
Course Code
AIC-1106
Available Formats
Classroom
Overview
Course Description
Run Google Antigravity as a multi-agent AI coding system rather than an autocomplete tool. AI coding agent architecture covers context windows, token limits, the plan-execute-iterate cycle, autonomous file and terminal operations, and the permission boundaries that keep agentic operation safe. Context engineering covers prompting strategies, file and codebase reference systems, extended-thinking budgets, and cross-session memory that shapes agent behaviour over a project’s lifetime. Configuration standards, the Model Context Protocol, and practical workflows cover GEMINI.md, AGENTS.md, exclusion strategies, MCP hosts and servers, permission management, code generation, review, testing, debugging, and refactoring. Antigravity product features cover Editor and Manager views, Mission Control multi-agent orchestration, browser sub-agents for visual verification, autonomy tiers, and auto-execution policies. Hands-on labs produce a configured environment, a project-level GEMINI.md, at least one MCP integration, and a multi-agent workflow orchestrated through Mission Control. The course is designed for software developers, data scientists, and anyone integrating a multi-agent AI coding system into daily team workflows.
Skills Gained
By the end of this course, participants will be able to:
- Apply prompt engineering and context-management techniques to Google Antigravity
- Configure Antigravity for IDE workflows and per-project conventions
- Drive code generation, refactoring, testing, and debugging through Antigravity’s agentic operation
- Integrate Model Context Protocol servers to extend Antigravity’s reach
- Evaluate Antigravity’s output against project quality and security standards
Who Can Benefit
This course is designed for:
- Software Developers
- Data Scientists & Analysts
- Anyone interested in coding agents
Prerequisites
Participants should enter this course with:
- Familiarity using Generative AI
- Practical Python experience
Organizational Objectives
This course assists organizations to:
- Accelerate first-draft code production through Mission Control multi-agent orchestration
- Reduce review cycles by codifying tool conventions in GEMINI.md and shared configuration
- Keep AI tool use inside permission boundaries via MCP server allowlists and reviewed config hierarchies
- Build a team-wide working knowledge of Google Antigravity in two days
Software
All attendees must have a modern web browser and an Internet connection.
Course Details
Course Details
Module 1 - AI Coding Agent Foundations
By the end of this module, you will be able to describe how modern AI coding agents are architected, recognize the plan-execute-iterate loop in action, and apply the permission and review controls that keep autonomous file, terminal, and Git operations safe to delegate.
- AI coding agent architecture and components
- Context windows and token limits
- Agentic operation and the plan-execute-iterate cycle
- Autonomous file, terminal, and Git operations
- Reviewing and accepting proposed changes
- Tool permissions and safety controls
- Hands-on Lab: Walk a coding agent through a small refactor end-to-end, exercising the permission and review controls before any change is committed.
Module 2 - Context Engineering
By the end of this module, you will be able to apply zero-shot, few-shot, and chain-of-thought prompting to a coding agent, attach relevant code and docs through reference systems, and budget extended thinking and persistent memory so the agent stays focused on the task.
- Context management fundamentals
- Prompting techniques (zero-shot, few-shot, chain-of-thought)
- Reference systems (@file, @codebase, @docs, @web)
- Extended thinking modes and budget allocation
- Memory and cross-session persistence
- Hands-on Lab: Refine a vague coding request into a focused prompt that pulls in the right reference context and budgets extended thinking appropriately.
Module 3 - Configuration Standards
By the end of this module, you will be able to author a project-level configuration file (CLAUDE.md, AGENTS.md, or tool-specific equivalent), organize the configuration hierarchy under version control, and apply exclusion strategies to keep sensitive material out of agent context.
- Configuration file hierarchy and version control
- Universal context configuration (e.g., CLAUDE.md) and cross-tool compatibility
- AGENTS.md format and conventions
- Tool-specific configuration files
- File exclusion strategies
- Hands-on Lab: Author a project-level CLAUDE.md (or AGENTS.md) that captures the team’s conventions, exclusions, and review expectations.
Module 4 - Model Context Protocol (MCP)
By the end of this module, you will be able to describe the MCP host, client, and server roles, install and configure an MCP server, recognize the primitives the protocol exposes, and apply permission management appropriate to the tools the server grants access to.
- MCP architecture (hosts, clients, servers)
- MCP primitives (tools, resources, prompts)
- Configuration and server installation
- Security considerations and permission management
- Hands-on Lab: Install one MCP server, exercise its primitives from a coding agent, and audit the permission surface it introduces.
Module 5 - Practical Workflows
By the end of this module, you will be able to drive a coding agent across realistic tasks — generation, review, testing, debugging, refactoring, documentation, and Git operations — and recognize where agentic operation belongs inside an existing development workflow.
- Code generation and multi-file scaffolding
- Automated code review and security scanning
- Test generation and test-driven workflows
- Agent-driven debugging and error diagnosis
- Refactoring patterns (single-file and multi-file)
- Documentation generation and Git integration with automated commit messages
- Hands-on Lab: Run a single feature ticket through generation, review, test, and documentation steps, then have the agent prepare the commit.
Module 6 - Google Antigravity Features
By the end of this module, you will be able to switch between Antigravity’s Editor and Manager views, orchestrate multiple agents through Mission Control, run browser sub-agents for visual verification, and configure autonomy tiers and auto-execution policies through GEMINI.md.
- Editor vs. Manager views and development mode selection (Agent-driven, Review-driven)
- Mission Control multi-agent orchestration and the agent monitoring dashboard
- Artifacts system (task plans, implementation plans)
- Browser sub-agents and visual verification workflows
- Autonomy tier configuration and auto-execution policies
- GEMINI.md configuration
- Hands-on Lab: Use Mission Control to coordinate two agents on the same project and verify their output through a browser sub-agent.
Schedule
FAQ
Does the course schedule include a Lunchbreak?
Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.
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Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.
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Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.
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As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.
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Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.
Do you provide hands-on training and exercises in an actual lab environment?
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Reviews
the class/lecture was amazing and very easy to understand and was in detail.
Very interactive and in-depth course that really got me ready for the industry
The labs and course material gave me valuable insights into cloud security architecture
Very good couse and again we would like to see more videos on removing FRUs
The class was very vast paced however the teacher was very good at checking in on us while giving us time to complete the labs.
