Agent Development Kit (ADK) Integrations

Gemini Enterprise Agent Platform is an advanced AI infrastructure from Google Cloud that enables organizations to build and manage intelligent agents at scale. As the evolution of Vertex AI, it consolidates model development, agent creation, and deployment into a unified platform. The system provides access to a diverse library of over 200 AI models, including cutting-edge Gemini models and leading third-party solutions. It supports both low-code and full-code development, giving teams flexibility in how they design and deploy agents. With capabilities like Agent Runtime, organizations can run high-performance agents that handle long-duration tasks and complex workflows. The Memory Bank feature allows agents to retain long-term context, improving personalization and decision-making. Security is a core focus, with tools like Agent Identity, Registry, and Gateway ensuring compliance, traceability, and controlled access. The platform also integrates seamlessly with enterprise systems, enabling agents to connect with data sources, applications, and operational tools. Real-time monitoring and observability features provide visibility into agent reasoning and execution. Simulation and evaluation tools allow teams to test and refine agents before and after deployment. Automated optimization further enhances agent performance by identifying issues and suggesting improvements. The platform supports multi-agent orchestration, enabling agents to collaborate and complete complex tasks efficiently. Overall, it transforms AI from a productivity tool into a fully autonomous operational capability for modern enterprises.

A comprehensive managed compute platform designed to rapidly and securely deploy and scale containerized applications. Developers can utilize their preferred programming languages such as Go, Python, Java, Ruby, Node.js, and others. By eliminating the need for infrastructure management, the platform ensures a seamless experience for developers. It is based on the open standard Knative, which facilitates the portability of applications across different environments. You have the flexibility to code in your style by deploying any container that responds to events or requests. Applications can be created using your chosen language and dependencies, allowing for deployment in mere seconds. Cloud Run automatically adjusts resources, scaling up or down from zero based on incoming traffic, while only charging for the resources actually consumed. This innovative approach simplifies the processes of app development and deployment, enhancing overall efficiency. Additionally, Cloud Run is fully integrated with tools such as Cloud Code, Cloud Build, Cloud Monitoring, and Cloud Logging, further enriching the developer experience and enabling smoother workflows. By leveraging these integrations, developers can streamline their processes and ensure a more cohesive development environment.

Gemini is Google’s next-generation AI assistant designed to deliver intelligent help across research, creativity, communication, and task management. Built on Google’s most advanced AI models, including Gemini 3, it helps users understand complex topics, generate content, and solve problems through natural conversation. Gemini enables text, image, and video generation, allowing users to quickly turn ideas into visual and written outputs. Its grounding in Google Search ensures responses are informed, relevant, and easy to explore further through follow-up questions. Gemini supports hands-free and conversational brainstorming through Gemini Live, making it useful for presentations, interviews, and idea development. With Deep Research, Gemini can analyze hundreds of sources and compile detailed reports in a fraction of the time. The platform connects directly to Google apps like Gmail, Docs, Calendar, Maps, and YouTube to streamline everyday workflows. Users can build personalized AI helpers using Gems by saving detailed instructions and uploaded files. Gemini’s long context window allows it to process large documents, code repositories, and research materials in a single session. Multiple plans provide flexibility, from free access for students and casual users to premium tiers with higher limits and advanced features. Gemini is available across web and mobile devices for seamless access. Designed to adapt to different needs, Gemini supports consumers, professionals, educators, and enterprises alike.

Agent2Agent (A2A) is a groundbreaking protocol introduced by Google to improve communication and collaboration between AI agents. This protocol allows AI systems to exchange tasks, data, and insights autonomously, making multi-agent workflows more efficient. A2A facilitates the seamless integration of different AI models, ensuring they work together in a synchronized manner, which is crucial for the development of advanced AI ecosystems. By supporting knowledge transfer between agents, A2A opens up new possibilities for complex, multi-step processes and smarter AI applications.

Gemini Enterprise app is a powerful enterprise-grade AI platform that enables organizations to deploy, manage, and scale AI agents across their entire workforce. It integrates seamlessly with popular productivity tools and data sources, allowing users to access and analyze business data through a single interface. The platform supports advanced automation by enabling agents to execute complex, multi-step workflows across multiple applications. It includes prebuilt agents like NotebookLM Enterprise, as well as tools for building custom and third-party agents using a no-code approach. Gemini Enterprise app provides robust security, governance, and compliance features, including data access controls, encryption, and regulatory support. It offers centralized visibility into all agents, workflows, and permissions, ensuring efficient management at scale. The platform is designed to enhance productivity across departments by automating repetitive tasks and accelerating content creation. It also helps break down data silos by connecting multiple data sources into one system. With scalable pricing options and enterprise-grade infrastructure, it supports both small teams and large organizations. Overall, Gemini Enterprise app delivers a unified, secure, and scalable solution for AI-driven business transformation.

asqav stands out as an innovative platform dedicated to the governance and security of artificial intelligence, ensuring that AI agents are consistently prepared for audits through real-time monitoring, enforcement, and a dependable log of every action taken. It boasts an efficient SDK that allows developers to seamlessly integrate governance capabilities into their AI agents with minimal code, enabling thorough oversight throughout the entire AI activity lifecycle. The platform also employs behavioral analysis to detect potential issues such as drift, exceeded rate limits, and scope violations, along with advanced threat detection systems that identify risks like prompt injections, leaks of sensitive data, and harmful outputs. Policy enforcement is facilitated by customizable “policy gates,” which establish specific rules for each agent, perform preflight evaluations, and offer dynamic approvals prior to any actions, ensuring that agents operate within defined boundaries. Moreover, asqav strengthens security with automated incident response functionalities that permit the suspension, isolation, or escalation of agents assessed as high-risk, thereby creating a comprehensive framework for maintaining accountability and safety in AI applications. Through these features, asqav not only protects AI operations but also fosters confidence in their use across a multitude of industries, thereby enhancing the overall efficacy and reliability of AI technologies. Ultimately, asqav serves as a crucial ally in the responsible deployment of AI, championing best practices in governance and security.

Agentspan represents a groundbreaking open-source server and SDK, designed to enhance the execution capabilities of AI agents by enabling them to operate in practical environments that extend beyond simple demonstrations. This platform allows developers to craft agents in Python and transform them into durable workflows that maintain execution state on the server, thus preventing any loss of progress during unexpected system failures or restarts. With this innovative structure, agents can pause and resume their activities precisely from where they left off, even when accessed from different devices. In addition, it promotes human oversight by allowing agents to pause for user consent and then seamlessly continue their operations through interfaces like Slack, web applications, or programming code. Furthermore, Agentspan accommodates intricate multi-agent workflows, permitting several agents to be linked within a single sequence while ensuring that every action is thoroughly logged, observed, and recoverable throughout the entire process. This holistic approach significantly boosts both the reliability and adaptability of AI applications across a variety of operational scenarios, ultimately transforming how AI agents function in real-world applications. As a result, developers and users alike can experience a more seamless integration of AI into their daily tasks.

AG-UI is a streamlined and open protocol designed for event-driven communication, providing a standardized way for AI agents to connect with user-centric applications. Its architecture prioritizes user-friendliness and flexibility, enabling effortless integration among AI agents, real-time user contexts, and diverse user interfaces. This protocol significantly improves the interaction between agents and humans by allowing backend systems to produce events that conform to AG-UI’s established event categories during the operations of the agents, as well as accepting simple inputs that are compatible with AG-UI. AG-UI functions effectively with various event transport mechanisms, including Server-Sent Events (SSE), WebSockets, webhooks, and additional streaming methodologies, featuring a versatile middleware component that ensures compatibility across multiple environments. Furthermore, AG-UI's integration of agents into applications focused on user engagement enriches the overall agent-centric protocol framework: while MCP provides agents with crucial functionalities, A2A promotes communication among agents, and AG-UI specifically connects agents to user interfaces. By adopting this holistic strategy, AG-UI plays a vital role in fostering enhanced interactions between users and AI technologies, ultimately paving the way for more intuitive user experiences. The adoption of AG-UI marks a significant step forward in the evolution of human-AI collaboration.

Agent Control is an innovative open-source framework that revolutionizes the management of AI agent behavior on a grand scale, establishing a new standard for governance in the field. It tackles the challenges posed by fragmented and hardcoded checks by equipping teams with a cohesive governance layer that applies regulations at every stage, all managed from a single control interface that can be dynamically updated without needing modifications to the agent's core code. Developers can easily identify any function for governance by using the control() decorator, turning critical decision points within an agent into separately regulated control points, each with tailored governance policies. When a function marked with this decorator is executed, Agent Control evaluates the input or output based on the relevant policy, generating responses that can include denying, steering, warning, logging, or allowing the action. Should a denial occur, the SDK raises a ControlViolationError, effectively blocking any potentially harmful actions from being carried out. This clear demarcation of policies from the actual code empowers developers to strategically position control hooks, while governance teams can focus on the specifics of enforcement, promoting a collaborative governance model. The adaptability and strength of Agent Control render it an essential resource for organizations aiming for effective standardization in AI agent governance, and its user-friendly interface further enhances accessibility for developers across various levels of expertise.

Silmaril represents a groundbreaking defense strategy against prompt injection, designed to autonomously repair itself in order to protect AI systems from complex, layered threats that traditional defenses often fail to address. Unlike standard techniques that simply filter out harmful inputs, it envelops inference requests, rigorously analyzing whether the series of actions could lead to adverse outcomes. Utilizing a multihead classifier, Silmaril assesses user motivations, application contexts, and execution states in parallel, enabling it to detect indirect injections, prolonged attack patterns, context alterations, and tool misuse before they can inflict damage. To bolster its protective features, Silmaril employs autonomous threat-hunting agents that navigate through systems, uncover vulnerabilities, and generate synthetic training data from real attack scenarios. This intelligence not only aids in automatic model retraining, allowing for the implementation of upgraded defenses in under an hour, but also ensures the distribution of anonymized protective strategies across all operational instances. Furthermore, this forward-thinking methodology guarantees that the system can maintain its resilience against new threats, continuously adapting to the shifting challenges in the cybersecurity landscape. By consistently evolving, Silmaril ultimately fortifies the security framework surrounding AI technology.