context: At its annual developer conference Google I/O 2026, Google introduced major advances in AI. gemini 3.5 flash model, gemini omni Physical world model and its new gemini spark Personal AI agent.
About AI agent:
What is it?
- AI agents are sophisticated software systems that leverage an underlying large language model (LLM) as a central cognitive processor to pursue specific goals and autonomously complete complex multi-step workflows.
- Unlike traditional static software or basic chatbots, AI agents do more than just answer queries. They exhibit independent reasoning, planning, memory management, and proactivity and are able to make decisions and perform digital transactions on behalf of users.
structure?
AI agents work by combining an underlying AI model with a specialized architectural framework.
- Brain (LLM Core): Parse natural language, process multimodal input (text, audio, video, code), and drive decision-making.
- Persona: Establish highly defined roles, communication styles, and behavioral constraints tailored to the task at hand.
- Memory system: Equipped with a structural memory layer that includes: short term (to maintain immediate conversational context), long term (for history logs), episodic (about past interactions), and consensus (For data shared between multiple agents).
- Tool integration: Teach agents how to connect to external APIs, databases, software applications, and web search engines to actively read, edit, and control external digital systems.
Main features:
- Reasoning and observation: Constantly observe your environment through computer vision and data feeds and use logic to draw inferences and adapt to changing conditions.
- Autonomous planning: Break down broad user goals into a series of steps, anticipate potential failures, and self-correct midway through the workflow.
- Collaboration and self-refinement: Working fluidly with humans and other digital agents, it continuously evaluates its own performance, fixes software bugs, and optimizes future output.
AI agent type:
| classification | Agent type | Core operating mechanism |
| by user interaction | surface agent | User-triggered conversational tools are built to directly assist humans with urgent questions and tasks (e.g., customer support, medical Q&A, etc.). |
| background agent | Event-driven workflow engines work behind the scenes with minimal or no human intervention to automate routine data pipelines. | |
| By number of structures | single agent system | Standalone units running on a single foundation model are ideal for highly contained and well-defined digital operations. |
| multi-agent system | A network of specialized agents, potentially running in different base models, that collaborate or compete to solve highly complex enterprise-level problems. |
application:
- Personal digital management: The AI assistant integrates with apps like Gmail, Docs, and Drive to manage your schedule, organize your files, and perform tasks across multiple apps.
- advanced cyber defense: AI systems can scan large codebases, detect software vulnerabilities, and automatically generate security patches.
- No-code engineering: Multi-agent AI platforms can create, test, and deploy software systems directly from textual instructions.
- Physics-driven media simulation: Advanced AI models understand movement and physics and can edit videos, change characters, and create interactive virtual environments.
