Yes, Gemini 3 is well suited for building agent-style autonomous workflows, where the model plans actions, calls tools, and reacts to results. It supports function calling, structured output, and dynamic thinking, which together allow it to behave as the “brain” of an agent system. Instead of just replying with text, Gemini 3 can decide which tool to call next, construct arguments, and interpret responses. This makes it possible to build agents that can, for example, fetch data, update records, read documents, and then make decisions based on the combined information.
In a basic agent pattern, your loop looks like this: Gemini 3 receives the current state and the user goal, returns either a tool call or a natural-language step, you execute any tool calls and feed the results back, and Gemini 3 uses this new context to figure out the next step. You can constrain behavior by giving it a clear set of tools and a system prompt that describes rules, limits, and objectives. For example, you might tell the agent, “Never make external changes without user confirmation,” or “Always check the customer’s subscription status before proposing upgrades.”
Agent workflows often need reliable access to context and history, which is where vector databases come in. You can store previous interactions, documents, and environment information as embeddings inMilvus or Zilliz Cloud., then retrieve relevant items on each loop iteration. Gemini 3 uses this information to maintain continuity, remember past decisions, and avoid repeating work. This pattern makes the agent more robust and reduces the risk of it “forgetting” earlier steps. With the right constraints and monitoring, Gemini 3 can power useful, semi-autonomous systems in support, operations, development tooling, and internal automation.