Build next-gen AI agents at Google Cloud Agentic AI Day. 30-hour hackathons and technical workshops across major Indian cities.
What Agentic AI Day Puts on the Table
Google Cloud Agentic AI Day is built around a simple idea: stop treating models as single-turn answer machines and start treating them as systems that plan, call tools, remember context, and act toward a goal. The day pairs technical workshops with 30-hour hackathons so teams can learn the patterns in the morning and ship a working agent before the clock runs out. Across major Indian cities, the same format gives local builders a shared venue to prototype without waiting for a remote demo or a slide deck.
An agent is not “chat with extra steps.” It is a loop: observe state, decide the next action, invoke an API or tool, evaluate the result, and either continue or stop. That loop only holds up when you design for failures—timeouts, bad tool output, partial plans—and when you cap cost and scope so a runaway agent cannot burn through tokens or touch the wrong system. The event is useful precisely because those constraints show up under time pressure, not only in architecture diagrams.
Workshops First, Then the 30-Hour Build
Technical workshops are the right place to lock down the stack before the hackathon starts. Focus on three layers: how the model is prompted and grounded, which tools it may call and under what auth, and how state is stored between steps. If you skip that order, teams often invent a clever demo that cannot be restarted, audited, or handed to someone else. Spend workshop time on interfaces and safety boundaries, not on polishing UI chrome.
A 30-hour hackathon rewards a narrow problem with a clear success signal. Pick a workflow you can measure—ticket triage, log summarization into a ticket, multi-step form completion, or retrieval over a small fixed corpus—and define “done” as a reproducible run from a clean state. Prefer one reliable tool chain over five half-wired integrations. Log every tool call and intermediate decision so judges and teammates can see why the agent acted, not only what it said.
Patterns That Survive Beyond Demo Day
Strong agent demos share a few design choices that transfer to production thinking even when the prototype is throwaway.
- Explicit goals and stop conditions — The agent should know when the task is complete and when to ask a human, instead of looping forever.
- Tool allowlists — Only the APIs and data sources needed for the story; no open-ended shell or broad write access.
- Human checkpoints on irreversible steps — Approvals for send, delete, purchase, or any external side effect.
- Observable traces — Step-level logs you can replay when the agent takes a wrong path.
If your team can explain the failure modes for each of those items, you are building an autonomous system, not a scripted chat flow. That distinction is what Agentic AI Day is meant to surface for builders who want next-gen agents rather than another chatbot wrapper.
How Teams in India Can Use the Multi-City Format
Events spread across major Indian cities lower the cost of showing up: you get peers, mentors, and a fixed window of deep work without relocating for a multi-day conference. Use the local cohort for pair debugging, shared eval sets, and honest critique of whether the agent is solving a real workflow or only impressing a live audience. After the hackathon, the durable artifact is the architecture note: problem definition, tools, guardrails, and what you would change with another week.
Come prepared with a domain you know, a small dataset or API you are allowed to use, and a story of who the agent serves. Leave with a runnable loop, a short write-up of tradeoffs, and a list of risks you did not have time to fix. That is how a day of workshops and a 30-hour build turns into lasting skill for autonomous systems—not a one-off demo that only works on stage.