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Create and manage custom Agent resources on Gemini Enterprise Agent Platform before running conversations. Prepare REST requests to configure instructions, attach files and skills, connect tools, track creation progress, and retrieve, update, or delete agents.

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• Create an Agent with task-specific instructions
• Attach files and skills from Cloud Storage or Skill Registry
• Configure connections to external MCP tools

Build conversations and workflows with the Gemini Interactions API on Gemini Enterprise Agent Platform. Follow Python or JavaScript examples for server-stored conversation history, streaming responses, structured JSON output, function calls, and background agent tasks.

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• Building chats that retain conversation context
• Displaying responses as they stream
• Returning JSON in a specified format

Set up the official Google Ads MCP Server, a connector that lets AI assistants retrieve campaign data and reporting metrics through natural language. The guide covers installation, credentials, client configuration, and connection checks.

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• Connecting Claude Code or Cursor to Google Ads
• Configuring API credentials and environment variables
• Testing campaign queries in natural language

Call Gemini and OpenMaaS models such as Llama, DeepSeek, and Qwen through Google Cloud Agent Platform. Prepare authentication and SDK code, run prompts, configure connection endpoints, and troubleshoot common request errors.

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• Send a test prompt to Gemini
• Call Llama or DeepSeek with the OpenAI SDK
• Write code to call an existing custom endpoint

Find and manage agent skills in the Gemini Enterprise Agent Platform Skill Registry, a catalog of available capabilities. The skill supports searching, uploading, updating, deleting, inspecting revision histories, checking operation status, and creating local starter files.

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• Search for skills that provide a needed capability
• Upload or update skills in the registry
• Inspect skill details and revision histories

Prepare to fine-tune open models or Gemini models on Agent Platform. Check the environment, convert and validate training data, choose supported models and settings, and estimate tuning costs for open models.

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• Checking whether an Agent Platform tuning environment is ready
• Converting and validating training datasets
• Choosing models and training settings for a task

Deploy Model Garden models to Agent Platform endpoints, the addresses used for model requests. Check supported hardware and estimated hourly costs, inspect deployment progress, test predictions, and remove deployed resources after use.

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• Choose a deployable model and supported hardware
• Estimate hourly deployment costs before approval
• Check deployment progress and test predictions

Investigate 403 and 500 errors in Google Cloud Gemini Enterprise Agent Platform. Examine logs, registered destinations, agent identities, and authorization policies to produce a diagnostic report with evidence, recommended fixes, and verification steps.

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• Investigating 403 errors when an agent calls an external API
• Diagnosing Agent Runtime 500 errors involving Model Armor
• Checking registration and permissions for failing MCP connections

Guides migration from Google AI Studio’s Gemini API to Gemini Enterprise Agent Platform on Google Cloud, covering billing, access permissions, authentication, and connection settings for SDKs and supported agent tools.

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• Moving a Gemini application to Google Cloud
• Using Google Cloud credits for Gemini API calls
• Unifying agent authentication and billing

Create images for Kortix blog headers, social posts, thumbnails, and other placements using its brand guidance. The workflow covers dimensions, image generation, logo and text compositing, and PNG export, with optimized WebP files for web use.

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• Creating a blog header or social link preview image
• Producing social cards and thumbnails
• Framing a screenshot of the real Kortix interface

Operate Gemini Notebook through notebooklm-py commands or its Python API. Set up authentication, organize notebooks and sources, ask questions grounded in those sources, and generate or download outputs such as audio, reports, quizzes, and slide decks.

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• Organizing research sources in notebooks
• Asking questions based on collected sources
• Generating audio or reports from source material

Guides AI agents through repeated failures, passive behavior, and incomplete work using structured investigation, alternative approaches, and evidence from actual checks. It pairs these practices with forceful coaching modeled on corporate performance reviews.

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• Breaking out of repeated failed approaches
• Requiring evidence before accepting completion
• Checking related issues after a fix

Helps AI agents recover from repeated failures by reviewing previous attempts, checking original sources, testing different hypotheses, and verifying results. It combines structured troubleshooting with performance review rhetoric modeled on large technology companies.

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• Breaking out of repeated failed attempts
• Investigating configuration or deployment failures
• Verifying an agent's claimed completion

Research topics across multiple sources, cross-check findings, and verify source URLs before producing a report with confidence labels. Choose from quick lookups to iterative investigations that preserve findings, with community sources prioritized for questions about public reactions.

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• Compare AI technology trends
• Map a market and its products
• Investigate community reactions

Create blog illustrations, technical diagrams, comparisons, comics, and icons through workflows tailored to each format. The skill guides composition, color, and model selection, then stages generated images for review before they are used in a project.

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• Creating blog header illustrations
• Explaining a system with a technical diagram
• Visualizing comparisons and timelines

Audit the technical factors that affect how search engines discover and index a website. Check crawl access, URLs, security, mobile pages, loading performance, and JavaScript rendering, then report measured results with prioritized fixes.

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• Investigate why pages are missing from search results
• Check technical SEO before a launch or site migration
• Review mobile content and page performance

Measure how often a brand appears in sampled answers across five AI platforms using SE Ranking. Compare platform-level percentages with sample-size confidence notes, and also inspect organic search rankings, backlinks, and competitor keyword gaps.

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• Compare brand visibility across five AI platforms
• Check the top 100 organic results for a keyword
• Review a website's backlink profile

Audit content for AI search by checking clear answers, supporting sources, author and brand information, and crawler access. Receive a readiness report with prioritized improvements and specific suggestions for rewriting passages.

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• Reviewing content for Google AI Overviews and other AI search
• Rewriting passages into clear, self-contained answers
• Checking crawler access for search and model training separately

Generate blog hero images, social previews, product photos, and infographics with Gemini through the banana extension. Apply settings suited to each use, then receive guidance on descriptive alt text, file naming, WebP conversion, and image metadata.

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• Create a social sharing preview image
• Generate a main image for a blog post
• Produce a product photo with a white background

Answer questions about videos from URLs or local files using timestamped visual and spoken evidence. Choose extracted frames and transcripts, or let Gemini view the full video with a configured API key.

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• Summarizing a video's visuals and speech
• Finding timestamps relevant to a question
• Checking a specific section of a long video

Review content for citation in AI search answers. Use checklists covering clear definitions, sourced data, author information, FAQs, and structured data, then plan how to monitor citations, brand mentions, and AI-referred traffic.

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• Reviewing articles for citation in AI search answers
• Organizing definitions, sources, and FAQs in content
• Checking author details and structured data

Develop mathematical proofs in Lean, a system that checks formal proofs, continue unfinished projects, or audit existing results. Check that the theorem matches the original claim, examine its assumptions, and prepare sources and commands for reproduction.

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• Turn a mathematical statement into a checked Lean proof
• Continue an unfinished Lean proof project
• Audit whether a theorem proves the original claim

Create model architecture diagrams and method illustrations for academic papers using Gemini. Codex plans the figure and reviews generated images for correct connections, content, layout, and readability, guiding repeated revisions toward a clearer final figure.

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• Illustrate a model architecture for a paper
• Show a method's processing steps and data flow
• Create a conceptual comparison diagram

Turn a research report and experiment results into an English paper with figures, LaTeX source, and a compiled PDF. Coordinate planning, drafting, Gemini reviews, and two revision rounds, preserving PDFs and an improvement log.

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• Turning a research report into a paper
• Drafting a paper from experiment results
• Preparing a paper for ICLR, NeurIPS, or ICML