GEAR: Google's skills track to build and ship AI agents with ADK
Enterprises are moving from AI agent pilots to production. To meet that demand, Google is launching the Gemini Enterprise Agent Ready (GEAR) program inside the Google Developer Program to help teams build, test, and operationalize agents using the Agent Development Kit (ADK).
The track combines hands-on labs, 35 recurring Google Skills credits each month, and badge-earning pathways so developers can learn by doing without up-front cost.
What's inside GEAR
- Hands-on labs: Practical projects that move from "hello world" to production patterns.
- 35 monthly Skills credits: Ongoing access to labs and sandboxes to keep momentum.
- Badges and progression: Proof of skill you can share internally or on your profile.
- Two initial pathways: "Introduction to Agents" and "Develop Agents with Agent Development Kit (ADK)." Expect coverage of agent anatomy, integration with Gemini Enterprise workflows, and ADK-based builds.
Why this matters for engineering and IT
Moving from prototype to production is where most AI initiatives stall. Deloitte's 2026 State of AI in the Enterprise report notes that about a quarter of 3,200 respondents said their organizations had moved roughly 40% of pilots into production. That gap is process and tooling.
GEAR tackles both: standard tooling (ADK), structured learning, and a cost buffer via Skills credits. The result is faster iteration, better test coverage, and a clearer path to scale.
Competitive context
Microsoft pushes AI learning paths and certifications via Microsoft Learn tied to Azure AI, with Azure OpenAI Service, Azure AI Studio, and Copilot Studio positioned for agent orchestration. AWS offers AI/ML and generative AI tracks through AWS Skill Builder and is promoting Bedrock Agents. Salesforce is embedding Agentforce in CRM workflows, while OpenAI's Assistants API is pitched as a flexible agent layer.
GEAR fits Google's broader play: make Gemini Enterprise and ADK a credible default for enterprise-grade agents.
Practical next steps for your team
- Enroll and explore: Start with the "Introduction to Agents" path to align on terminology and patterns. Google Developer Program
- Build a minimal agent: Use ADK to implement one narrow task (retrieval, ticket triage, or form fill). Keep the scope tight and observable.
- Wire into systems: Connect to your existing APIs, data stores, and event buses. Treat the agent like any other service.
- Add evaluation gates: Define acceptance tests: intent accuracy, tool-call success, latency, cost per request, and failure modes.
- Plan for operations: Set SLOs, tracing, red-teaming, and rollback paths. Decide who owns incident response.
- Lock down data: Enforce PII handling, prompt/content filtering, and least-privilege access for tools and connectors.
What good looks like in production
- Clear task boundaries with deterministic tool calls and guardrails.
- Automated evals in CI/CD to prevent regressions.
- Telemetry on latency, token usage, and tool-call outcomes.
- Fallback strategies and human-in-the-loop for edge cases.
Watch-outs
- Vendor lock-in: Abstract tool interfaces where possible so you can swap models or providers.
- Hidden costs: Track token usage and tool-call load; set budgets and alerts early.
- Data exposure: Scrub prompts/completions, restrict training data reuse, and log responsibly.
- Evaluation debt: No evals = silent drift. Bake benchmarks into your pipeline from day one.
If you're building a broader skills plan
GEAR is a strong path for Google Cloud and Gemini-centric teams. If you need cross-vendor coverage for mixed stacks, explore curated catalogs by provider to keep learning aligned with your platform mix. See AI courses by leading companies
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