By Sagar Shankaran, Founder of CallSphere
Vertex AI Agent Garden ships 80+ pre-built agents for sales, support, finance, and ops — here is what is actually useful. Practical context for teams in Washington.
Key takeaways
Agent Garden is Google's bet that most enterprises do not want to build agents from scratch — they want a curated catalog.
This briefing is written with builders in Washington in mind — local procurement, latency from regional Google Cloud / AWS / Azure regions, and time-zone-friendly support windows shape the practical recommendations.
Google's April 2026 cadence around the Gemini 3 family, Antigravity, and the AgentSpace surface is the most coherent product narrative the company has put together in years. The pieces fit: a frontier model (Gemini 3 Pro), a fast variant (Gemini 3 Flash), an on-device tier (Gemini Nano), an IDE (Antigravity), an agent runtime (Vertex Reasoning Engine), an agent catalog (Agent Garden), an enterprise hub (AgentSpace), and a consumer notebook (NotebookLM Pro). For builders, the practical impact is that you can pick a Google story for almost any agent shape and have a credible delivery path from prototype to production.
On SWE-bench Verified, Gemini 3 Pro scores 71.8% — within striking distance of Claude Opus 4.7's 72.9% and ahead of GPT-5.5's 69.4%. On tau-bench retail, the new model lands at 95.1%, a meaningful jump from Gemini 2.5's 88.6%. MMMU sits at 84.0%. The numbers matter less than the spread: for the first time, the three frontier labs are within 3 percentage points of each other on most benchmarks that builders cite.
For Washington teams, the practical near-term move is to set up an evaluation harness against your top 3 production prompts before committing to a model swap.
Gemini 3 Pro is priced at $1.25 / $10.00 per million input/output tokens up to 200K context; long-context (>200K) tier kicks in at $2.50 / $15.00. With prompt caching at a 75% discount and a 50% Batch API discount on async workloads, the realized cost for many production agents lands closer to $0.80 per million blended tokens. Compared to Claude Opus 4.7 ($15/$75) and GPT-5.5 ($10/$30), Gemini 3 Pro is positioned as the price-aggressive frontier option.
The recommended path is prototype in AI Studio, then promote to Vertex AI for production. Vertex provides regional availability (12 regions globally, including europe-west4 and asia-southeast1), VPC-SC, CMEK, audit logging, and the new Reasoning Engine managed runtime. AI Studio's prompt IDE got a major refresh — versioned prompts, side-by-side eval, and one-click deployment to Vertex are now first-class.
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This is the short version; the full vendor documentation has more nuance, particularly on rate limits and regional availability.
A migration without answers to these questions is a Q4 incident report waiting to happen:
Q: Is Gemini 3 Pro available in my region?
A: Gemini 3 Pro is generally available in 12 Vertex AI regions as of May 2026, including us-central1, europe-west4, asia-southeast1, and asia-northeast1. Check the Vertex AI region availability docs for the latest list.
Q: How does Gemini 3 Pro pricing compare on a real workload?
A: Headline price is $1.25 / $10.00 per million tokens up to 200K context. With 75% prompt cache discount and 50% Batch API discount, realized blended cost on long-running agent workloads typically lands at $0.80-$1.20 per million tokens.
Q: Can I use Antigravity with Claude or GPT-5.5?
A: Yes. Antigravity is unusually open — Claude Opus 4.7, GPT-5.5, and Gemini 3 Pro are all first-class providers in the IDE settings.
Q: What is the difference between A2A and MCP?
A: MCP is the agent-to-tool protocol; A2A is the agent-to-agent protocol. They are complementary, not competitive — most production agent stacks will use both.
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Last reviewed 2026-05-05. Pricing and benchmarks change frequently — check primary sources before relying on numbers in this article.
Most coverage of Vertex AI Agent Garden: 80+ Pre-Built Agents Explained stops at the press release. The interesting part is the implementation cost — what changes for a team running 37 agents and 90+ tools in production? The CallSphere stack treats announcements as input to an evals queue, not a product roadmap. Production agents stay pinned; new releases earn their slot only after a regression suite confirms cost, latency, and tool-call reliability move the right way.
Google's AI position spans three layers worth keeping straight: the Gemini family (general-purpose multimodal models), Vertex AI (the managed runtime, MLOps tooling, and enterprise-grade governance around them), and a growing set of vertical plays (Med-PaLM-class healthcare models, retail-specific search, document-AI for ops). For SMB call automation, the realistic Gemini fit today is post-call analytics, multimodal document handling (insurance card photos, ID verification, receipts), and longer-context summarization — not the realtime audio inner loop, where streaming stability and tool-call latency still favor incumbent realtime APIs. Vertex AI is where the enterprise governance story lives: VPC service controls, regional pinning, audit logging, and IAM that maps cleanly onto an existing GCP estate. CallSphere's evaluation pattern for Google AI: keep Gemini in the analytics evals queue, lean on Vertex when a customer's compliance posture requires GCP-native data residency, and re-evaluate the realtime story on every major release. Google's vertical-AI plays are worth tracking because they signal where the specialist-model market is headed.
Q: Is vertex AI Agent Garden ready for the realtime call path, or only for analytics?
A: Most of the time it doesn't, and that's the right starting assumption. The relevant test is whether it improves at least one of: p95 first-token latency, tool-call argument accuracy on noisy inputs, multi-turn handoff stability, or per-session cost. The CallSphere stack — Twilio + OpenAI Realtime + ElevenLabs + NestJS + Prisma + Postgres — is sized for fast turn-taking, not raw model size.
Q: What's the cost story behind vertex AI Agent Garden at SMB call volumes?
A: The eval gate is unsentimental — a regression suite that simulates real call traffic (noisy ASR, partial inputs, tool-call timeouts) measures four numbers, and a candidate has to win on three of four without losing badly on the fourth. Anything else is treated as a blog post, not a stack change.
Q: How does CallSphere decide whether to adopt vertex AI Agent Garden?
A: In a CallSphere deployment, new model and API capabilities land first in the post-call analytics pipeline (lower stakes, async, easy to roll back) and only later in the live realtime path. Today the verticals most likely to absorb new capability first are After-Hours Escalation and IT Helpdesk, which already run the largest share of production traffic.
Want to see salon agents handle real traffic? Walk through https://salon.callsphere.tech or grab 20 minutes with the founder: https://calendly.com/sagar-callsphere/new-meeting.
Written by
Sagar Shankaran· Founder, CallSphere
Sagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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