Want to get building production-ready AI agents? Here’s where startups should start.

Startups are using agentic AI to automate complex workflows, create novel user experiences, and solve business problems that were once considered technically impossible. Still, charting the optimal path forward — especially with the integration of AI agents — often presents significant technical complexity

To help startups navigate this new landscape, we’re launching our Startup technical guide: AI agents. It provides a systematic, operations-driven roadmap for embracing the potential of agentic systems.

What does this potential look like? AI agents combine the intelligence of advanced AI models with access to tools so they can take actions on your behalf, under your control. Unlike traditional AI, agentic AI can break down intricate tasks, refine plans, and dynamically utilize external resources and tools. The key takeaway is that AI agents can tackle complex, multi-step problems, ultimately transforming from a passive tool into a proactive problem-solver.

If your startup is looking to get in on the agentic AI action, here are some initial steps to consider. And when you’re ready to get building, you can get more details in our guide or even reach out to one of our AI experts at Google Cloud.

Choose your path: Build, use, or integrate

Every startup’s journey is unique, which is why Google Cloud offers a flexible agent ecosystem that supports the comprehensive development of agentic systems. You can:

• Build your own agents: For teams that require a high degree of control over agent behavior, the open-source Agent Development Kit (ADK) is your go-to development framework. ADK is built for a custom, code-first approach, empowering developers to build, manage, evaluate, and deploy AI-powered agents. For an application-first approach, Google Agentspace orchestrates your entire AI workforce and empowers non-technical team members to build custom agents using a no-code designer.
• Use Google Cloud agents: With rapid prototyping and easy ways to integrate AI into your existing apps, managed agents let you focus on core business logic rather than managing infrastructure. Gemini Code Assist is an AI-powered assistant for developers, while Gemini Cloud Assist is an AI expert for your Google Cloud environment.
• Bring in partner agents: For more specialized use cases, you can easily integrate third-party or open-source agents into your stack via the Google Cloud Marketplace. You can also explore the Agent Garden to deploy prebuilt ADK agents that already support data reasoning and inter-agent collaboration.

No matter which path you choose, our ecosystem is designed for interoperability, built on open standards like the Model Context Protocol (MCP) and the Agent2Agent (A2A) protocol.

4 steps for building your first agent

The Startups technical guide: AI agents provides a complete roadmap for building production-ready AI agents. Here’s four core steps we’ve identified that can help define your first agent, using Agent Development Kit (ADK).

Step 1: Give your agent an identity

First, define your agent’s core identity. You’ll want to give it a unique name for logging and delegation, a clear description of its capabilities so other agents can route tasks to it, and identify the right AI foundation model (like Gemini 2.5 Pro or Gemma) to power its reasoning. Precision here is critical. The model you’re using treats every part of this definition as a prompt, and vague descriptions can lead to “context poisoning,” causing the agent to pursue incorrect goals.

Step 2: Write the “prime directive” with instructions

Next, give your agent its “prime directive” using the instruction parameter. This is where you define its persona, core objectives, and do’s and don’ts. Effective instructions clearly specify the desired outcomes for your agent, provide examples for complex tasks (e.g. few-shot prompting), and guide the agent on how to use its tools.

Step 3: Grant superpowers with tools

Transform your agent from a pure conversationalist into a system that can take action by equipping it with functions to call external APIs, search databases, or interact with other systems. In doing so, you grant it broader capabilities. For example, a bug assistant agent uses tools to fetch user details from a CRM or create a ticket in a project management system. Since the agent chooses which tool to use based on its name and description, making them clear and unique is crucial to avoid looping behaviors or incorrect actions.

Step 4: Master the lifecycle: test, deploy, operate

Building an agent is a continuous cycle, not a one-off task. Because agentic systems are non-deterministic, standard unit tests are insufficient. Our guide shows you how to evaluate an agent’s “trajectory” — its step-by-step reasoning — to ensure quality and reliability. This operational rigor, which we call AgentOps, is key to confidently deploying your agent on platforms like Vertex AI Agent Engine or Cloud Run and operating it safely in production.

Agents already in action

Startups are constantly innovating their agentic journeys , here’s a look at two startups that use Google Cloud’s models and architecture to run their agentic systems:

Actionable insights for better employee engagement

Wotter, a provider of next-generation Employee Engagement solutions, seeks to better understand what employees want and empower organizations with the insights they need to get the best out of their people by asking the right question to the right person at the right time.

Gemini 2.5 Flash was the right foundation model for Wotter’s smart assistant, blending speed with long-context reasoning. Wotter’s Flash models use agentic methods to manage extensive and ongoing sources of data, such as employee interactions and feedback, while still responding to queries on this data in seconds – and at a lower cost per query.

Eliminate a long-standing legal industry pain point

As people in the legal industry know too well, complex document reviews can ruin nights and weekends while turning focus away from strategic work. Enter Harvey, which is equipping legal professionals with domain-specific AI to maximize efficiency and keep legal professionals’ attention on activities that move the needle for their firms and clients.

Harvey evaluated several foundation models and ultimately found that Gemini 2.5 Pro achieved the leading score of 85.02% on its BigLaw Bench benchmark, the first of its kind to represent complex legal tasks. Gemini 2.5 Pro showcased strong reasoning across inputs consisting of hundreds of pages of materials—a common scenario in legal work. The model then used these materials to generate longer-form and comprehensive outputs, enabling deeper insights and analyses.

These core capabilities proved Gemini 2.5 Pro’s potential across complex legal work that requires reasoning over large sets of documents to support diligence, review, and use case drafting. Further, Vertex AI provides the stringent security and privacy guarantees that build trust in the Harvey platform among clientele. Gemini and Vertex AI are now an important part of Harvey’s vision for future product development.

Build what’s next, faster

The Startup technical guide: AI agents provides the blueprint your team needs to turn your vision into a production-ready reality. By using a code-first framework like ADK and the operational principles in this guide, you can move beyond informal “vibe-testing” to a rigorous, reliable process for building and managing your agent’s entire lifecycle. For your startup, this disciplined approach becomes a powerful competitive advantage.

No matter where you are with AI adoption, we are here to help. Contact our Startup team today and you could get up to $350,000 USD in cloud credits with the Google for Startups Cloud Program,