The "A-Team" has arrived: Why Google’s new AI agents are more than just chatbots

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For the past year, we’ve been obsessed with “chat.” We learned to chat with our documents (RAG), chat with our code (Gemini Code Assist), and chat with our data. But a recent flurry of announcements from Google Cloud, centered on Vertex AI Agent Builder and the new Gemini models, signals a fundamental shift.

We’re officially moving from chatbots to do-bots.

My opinion? This is the start of the “autonomous enterprise.” Google isn’t just giving us a smarter brain (Gemini); it’s giving that brain a body, hands, and a toolkit to work across our entire, messy, multi-cloud enterprise. The new Agent Development Kit (ADK), the Agent Engine for production, and the Agent2Agent (A2A) protocol aren’t just features—they are the building blocks for a new class of digital worker.

Let’s break down what this really means and how you can use it.

The “so what?” – From answering to acting

For a long time, an “AI agent” was a fancy FAQ. You’d ask it a question, and it would retrieve a grounded answer from a PDF or database.

The “latest” model of agents is different. Powered by Gemini’s advanced reasoning, these agents can understand a complex, multi-step goal, plan a series of actions, use multiple “tools” (like APIs, databases, or even other agents), and execute that plan to completion.

  • Old way (RAG): “What was our Q3 revenue in the EMEA region?”
    • Agent: Looks up a “Q3_revenue.pdf” and answers: “Our Q3 EMEA revenue was $5.2 million.”
  • New way (agentic workflow): “Our Q3 EMEA revenue seems low. Compare it to Q2, identify the top three underperforming product SKUs in the region, and draft an email to the EMEA sales director summarizing the gap.”
    • Agent:
      1. (Tool: BigQuery) Queries the sales database for Q3 EMEA revenue.
      2. (Tool: BigQuery) Queries the sales database for Q2 EMEA revenue.
      3. (Brain: Gemini) Performs a delta calculation.
      4. (Tool: BigQuery) Runs a new query to group SUM(sales) by sku where region = ‘EMEA’ and quarter = ‘Q3’, ordering by the gap.
      5. (Brain: Gemini) Synthesizes the findings and drafts a professional email.
      6. (Tool: Gmail API) Presents the draft to you for approval.

This is no longer a simple Q&A. This is an automated business analyst. Here are three practical examples you can build today.

Example 1: The “smart” DevOps agent (the on-call hero)

Your team gets a PagerDuty alert at 3 AM. The “checkout-service” is down. Instead of a groggy engineer fumbling with kubectl, they interact with an agent.

The goal: “A P0 alert just fired for ‘checkout-service’ in production. Find out why and what the impact is.”

How it’s built (Vertex AI Agent Builder + GKE Tools):

  1. Brain: A Gemini model (e.g., gemini-2.5-pro) is the agent’s core.
  2. Tools (APIs): You give the agent access to your existing tools:
  • get_gke_logs: A tool that connects to Cloud Logging and filters for the “checkout-service” pods.
  • get_pod_status: A tool that runs kubectl describe pod on the failing pods.
  • get_monitoring_dashboard: A tool that fetches the last 15 minutes of data from a Cloud Monitoring dashboard (e.g., CPU, memory, 5xx errors).
  1. The agentic workflow in action:
  • User: “P0 on ‘checkout-service’. What’s happening?”
  • Agent (Brain): “I need to diagnose a GKE service. My plan is to 1) check pod status, 2) check logs, and 3) check monitoring.”
  • Agent (Action): Calls get_pod_status.
  • Agent (Observation): “Pods checkout-7b... are in a CrashLoopBackOff state.”
  • Agent (Brain): “A crash loop. The logs will tell me why.”
  • Agent (Action): Calls get_gke_logs.
  • Agent (Observation): “Logs show a repeating error: Error: failed to connect to 'redis-primary.default.svc.cluster.local'.”
  • Agent (Brain): “It’s a database connection failure. This is a critical dependency. I should check the Redis pod, too.”
  • Agent (Response): “I’ve found the issue. The ‘checkout-service’ pods are in a CrashLoopBackOff because they can’t connect to the Redis primary. This suggests a failure in the Redis service, not a code bug. I am now investigating the ‘redis-primary’ pod.”

This agent didn’t just find an answer; it diagnosed a problem by orchestrating multiple tools in a logical sequence, just like a senior SRE would.

Example 2: The “multi-cloud” FinOps agent (the cost cutter)

Your VP of Engineering asks a simple question: “Why is our cloud bill 20% over budget this month?” The problem? Your “cloud bill” is spread across GCP, AWS, and Azure.

The goal: “Analyze our over-budget cloud spend and find the root cause.”

How it’s built (Vertex AI Agent Builder + Multi-Agent Protocol):

This is where it gets really powerful. Instead of one agent, you build three specialist agents and one manager agent that uses the new Agent2Agent (A2A) protocol.

  1. gcp-finops-agent: Has tools to query your BigQuery billing exports.
  2. aws-finops-agent: Has tools to query the AWS Cost Explorer API.
  3. azure-finops-agent: Has tools to query the Azure Cost Management API.
  4. finops-manager-agent: The user talks to this agent. It has no tools of its own, only the ability to talk to the other three agents.

The agentic workflow in action:

  • User: “Why are we 20% over budget this month?”
  • finops-manager-agent (Brain): “My goal is to find the source of the budget overrun. I need to get the spend data from all three clouds. I will delegate these tasks.”
  • finops-manager-agent (Action):
    1. (To gcp-finops-agent) “What is our total spend and biggest driver of overage this month?”
    2. (To aws-finops-agent) “What is our total spend and biggest driver of overage this month?”
    3. (To azure-finops-agent) “What is our total spend and biggest driver of overage this month?”
  • Specialist agents (simultaneously):
    1. gcp-finops-agent responds: “GCP spend is $110k, driven by an un-tagged BigQuery project (project-data-science-temp) that ran $30k in analysis queries.”
    2. aws-finops-agent responds: “AWS spend is $80k, driven by $25k in S3 egress fees, which are 50% higher than last month.”
    3. azure-finops-agent responds: “Azure spend is $30k, which is on-budget.”
  • finops-manager-agent (Brain): “I have the data. The total overage is $55k. The drivers are a rogue BigQuery project and unexpected AWS egress.”
  • finops-manager-agent (Response): "We are over budget by $55k. The primary drivers are:
    1. GCP: A $30k overspend from the ‘project-data-science-temp’ BigQuery project.
    2. AWS: A $25k overspend in S3 data egress fees, 50% above the norm."

Example 3: The “proactive” E-commerce agent (the customer saver)

This agent isn’t triggered by a user. It’s triggered by an event. It monitors a Pub/Sub topic for new, high-value orders and proactively manages them.

The goal: “Ensure all orders over $1,000 are fulfilled successfully and delight the customer.”

How it’s built (Vertex AI Agent Engine + Cloud Run):

  1. Trigger: A Cloud Function listens to a Pub/Sub topic (new-orders). When an order > $1,000 comes in, it triggers the agent in Vertex AI Agent Engine.
  2. Tools:
  • check_inventory: (Connects to Spanner/SQL) Confirms stock.
  • check_shipping_api: (Connects to an external API) Gets a delivery estimate.
  • create_support_ticket: (Connects to Zendesk/Salesforce) Creates a “white glove” tracking ticket.
  • send_email: (Connects to SendGrid/Gmail) Sends a custom email.

The agentic workflow in action (no user input):

  1. Event: A new order, #86753, for $2,500 is received.
  2. Agent (Brain): “New high-value order. My plan is to 1) verify stock, 2) get a shipping date, 3) create an internal tracking ticket, and 4) send a thank-you email to the customer.”
  3. Agent (Action): Calls check_inventory for the SKUs.
  4. Agent (Observation): “All items are in stock.”
  5. Agent (Action): Calls check_shipping_api.
  6. Agent (Observation): “Estimated delivery: 2 days (Oct 28th).”
  7. Agent (Action): Calls create_support_ticket with the note “High-value order #86753. Monitor for delivery on Oct 30th.”
  8. Agent (Action): Calls send_email with a Gemini-generated message: “Hi [Customer Name], thank you for your order! We’ve confirmed your items are in stock and your order #86753 is expected to arrive on October 28th. We’ve also assigned a dedicated support representative to monitor it until it arrives at your door.”
  9. Agent (Brain): “Plan complete. The order is processed, and the customer has been proactively notified.”

My final take

The new Gemini models provide the “smarts,” but the Vertex AI Agent Builder provides the “hands.” This is the real story. We’re about to see a Cambrian explosion of specialized AI agents that don’t just answer our questions but actively manage our cloud infrastructure, optimize our costs, and run our business processes.

If you’re a developer or architect, stop thinking about prompts. Start thinking about tools, goals, and workflows. Your job is no longer just to build apps—it’s to build the autonomous team that will run them.

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