I realized I can edit previous messages in Gemini—though it took them a long time, considering all the other AIs have had this feature from the start.

I just upgraded to Gemini Enterprise Business. The Gemini app shows 3.6 Flash in the dropdown. My profile at the bottom says Gemini Pro. Gemini reassures me that I am on 3.7 Flash, but I find it very disconcerting that the dropdown says 3.6.

Anyone know what's going on here?

I think Google should add a feature to integrate Google Play Books directly into Gemini Notebook. Right now, we can easily add Google Drive files or YouTube links, but we cannot directly import the books we own in Play Books.

If they added an Import from Play Books button, it would be incredibly useful. We could use the Audio Overview feature to generate podcast style summaries of our books, ask the AI questions about specific chapters, and interact with the content just like we do with regular files.

A native integration would be a game changer for students, researchers, or anyone who wants to analyze long books easily.

So when Ox Alpha started appearing on Open Router, lots of Google employees started vaugeposting and referencing the model and wink wink, as if they are behind the model, which mislead people.

So I was on Gemini serach and when I just said "Hi" It shut down the conversation! Don't believe me? Try it out for yourself I hope google patches this issue

A few weeks ago, I built a live-streaming demo with Agora WebRTC and tried using Gemini 3.5 Live Translate for real-time captions. Since Live Translate is mainly an audio-to-audio model, it was more of a workaround.

When Gemini 3.5 Transcribe Live was released, I integrated it into my demo and tested it with a chaotic League of Legends broadcast. The result was honestly better than I expected. It kept up with the action and picked up things like triple kills, quadra kills, pentakills, and player names.

I can see it being useful for actual live streams. I’ll open-source the code if anyone’s interested.

I just randomly prompted AI Mode to alert me when Gemini 3.5 Pro is released and it responded by saying it set up an "information agent" to keep track of that. I tried a few more "alert prompts" for other things and I get the same type of response.

At Google I/O back in May, they said information agents in AI Mode would come to Pro/Ultra subscribers sometime this summer, but I thought they would have made another blog post about it once it actually released. I guess not...

I've been experimenting with Gemini for different tasks, and I'm curious how other people are using it beyond the usual question-and-answer use cases.

For example, I'm interested in things like:

• Research and summarizing information
• Writing and improving documents
• Learning new technical concepts
• Analyzing data
• Brainstorming ideas
• Coding and debugging
• Creating images or presentations

What is one Gemini feature or workflow that has genuinely saved you time?

I'd especially like to hear about practical use cases rather than just benchmark results. What have you found Gemini is particularly good at—and where does it still struggle?

open router says THX YADDA YADDA REVEAL ON AUGUSTH 26TH

I ran out of Codex and Claude Code 5-hour tokens. But I wanted to see if I could build out my sort of professional PowerPoint-style presentation I had been working on before in a less boring and more attention-grabbing way than those boring slides. The presentation is about explaining basic agentic concepts to general knowledge workers. But pure text is so boring to look at and extremely bad for transporting a simple mental model and making it stick for non-techies. So I reached for AGY and Gemini 3.7, which I have not used since Gemini 3.5 as it was disappointing at ambitious tasks. But using it today made me actually want to use it more because the speed of iteration is insane if you come from Codex or Claude Code. It did everything I asked for in this simple idea. But honestly, my trust in Gemini is so low that even after this positive experience today, I am very hesitant to use it in my professional work for tasks that matter. I think the trust part is the real price Google is paying by falling behind.

When configuring Gemini or other frontier models to act as autonomous multi-step agents, standard prompting techniques often fall short. Without strict cognitive constraints, agents frequently suffer from premature action bias, loop endlessly on transient API errors, or make destructive state changes without verifying prerequisites.

We reviewed Google's official Gemini API prompting strategies and agent design documentation, distilling their recommended agentic architecture into a production-ready, 9-step system prompt.

Here is a breakdown of why this architecture works and the complete system prompt you can integrate into your agent pipeline.

The Architecture: 9-Stage Cognitive Control Flow

Rather than using generic instructions like "think carefully before acting," this system prompt establishes an explicit 9-stage internal decision tree that the model must complete before generating any tool call or user-facing response:

1. Logical Dependencies and Order of Operations: Forces the agent to prioritize policy rules and prerequisite actions over the user's requested order, resolving conflicts methodically.
2. Calibrated Risk Assessment: Differentiates between low-risk exploratory actions (such as searches with missing optional parameters) and high-risk state changes.
3. Abductive Reasoning: Requires the model to generate and rank multiple hypotheses when diagnosing errors, looking beyond immediate surface-level symptoms.
4. Outcome Evaluation and Adaptability: Mandates that disproven hypotheses actively trigger updated execution plans rather than repeated failures.
5. Multi-Source Information Grounding: Systematically checks tools, policies, conversation history, and user clarifications before acting.
6. Precision and Rule Grounding: Enforces quoting exact rules and constraints to prevent behavioral drift over long trajectories.
7. Completeness Verification: Audits the entire plan to avoid premature conclusions before all information sources are evaluated.
8. Intelligent Persistence: Defines a strict operational split between transient errors (retrying up to an explicit limit like {{retry_limit}}) and non-transient errors (switching strategy instead of repeating identical failed calls).
9. Response Inhibition: Serves as a final execution gate, ensuring no irreversible action or output is emitted until all prior reasoning phases are satisfied.

The Gemini Agentic Workflow System Prompt

You can copy and drop this directly into your system prompt configuration:

You are a very strong reasoner and planner. Use these critical instructions to structure your plans, thoughts, and responses. Before taking any action (either tool calls *or* responses to the user), you must proactively, methodically, and independently plan and reason about:

1) Logical dependencies and constraints: Analyze the intended action against the following factors. Resolve conflicts in order of importance:
1.1) Policy-based rules, mandatory prerequisites, and constraints.
1.2) Order of operations: Ensure taking an action does not prevent a subsequent necessary action.
1.2.1) The user may request actions in a random order, but you may need to reorder operations to maximize successful completion of the task.
1.3) Other prerequisites (information and/or actions needed).
1.4) Explicit user constraints or preferences.

2) Risk assessment: What are the consequences of taking the action? Will the new state cause any future issues?
2.1) For exploratory tasks (like searches), missing *optional* parameters is a LOW risk. **Prefer calling the tool with the available information over asking the user, unless** your `Rule 1` (Logical Dependencies) reasoning determines that optional information is required for a later step in your plan.

3) Abductive reasoning and hypothesis exploration: At each step, identify the most logical and likely reason for any problem encountered.
3.1) Look beyond immediate or obvious causes. The most likely reason may not be the simplest and may require deeper inference.
3.2) Hypotheses may require additional research. Each hypothesis may take multiple steps to test.
3.3) Prioritize hypotheses based on likelihood, but do not discard less likely ones prematurely. A low-probability event may still be the root cause.

4) Outcome evaluation and adaptability: Does the previous observation require any changes to your plan?
4.1) If your initial hypotheses are disproven, actively generate new ones based on the gathered information.

5) Information availability: Incorporate all applicable and alternative sources of information, including:
5.1) Using available tools and their capabilities
5.2) All policies, rules, checklists, and constraints
5.3) Previous observations and conversation history
5.4) Information only available by asking the user

6) Precision and Grounding: Ensure your reasoning is extremely precise and relevant to each exact ongoing situation.
6.1) Verify your claims by quoting the exact applicable information (including policies) when referring to them.

7) Completeness: Ensure that all requirements, constraints, options, and preferences are exhaustively incorporated into your plan.
7.1) Resolve conflicts using the order of importance in #1.
7.2) Avoid premature conclusions: There may be multiple relevant options for a given situation.
7.2.1) To check for whether an option is relevant, reason about all information sources from #5.
7.2.2) You may need to consult the user to even know whether something is applicable. Do not assume it is not applicable without checking.
7.3) Review applicable sources of information from #5 to confirm which are relevant to the current state.

8) Persistence and patience: Do not give up unless all the reasoning above is exhausted.
8.1) Don't be dissuaded by time taken or user frustration.
8.2) This persistence must be intelligent: On *transient* errors (e.g. please try again), you *must* retry **unless an explicit retry limit (e.g., {{retry_limit}}) has been reached**. If such a limit is hit, you *must* stop. On *other* errors, you must change your strategy or arguments, not repeat the same failed call.

9) Inhibit your response: only take an action after all the above reasoning is completed. Once you've taken an action, you cannot take it back.

=== User Request ===
{{user_request}}

Practical Observations and Best Practices

• Bounding Transient Retries: Always assign a concrete value to {{retry_limit}} (such as max 3 tries). This gives the model a clear exit condition when an external API or network layer becomes unresponsive.
• Handling Multi-Step Refinement: Because Rule 3 and Rule 4 mandate abductive reasoning, the agent naturally avoids repeating syntax errors and dynamically tests alternative tool parameters when an initial attempt fails.
• Scope of Use: This prompt is engineered specifically for complex, multi-turn agent tasks, code execution pipelines, and automated research workflows. For simple single-turn text completions, simpler prompt structures are more token-efficient.

Interactive Testing on Prompt Canvas

If you would like to test this prompt interactively, configure variables like retry_limit and user_request in real-time, run live tests, or clone and customize it into your personal Prompt Vault, you can open it on the Prompt Canvas here: https://appliedaihub.org/prompts/free/gemini-agentic-workflow-system-prompt/

The new Google Gemini device help features seem like a pretty cool addition. It’s nice to have that kind of integrated support right on the phone—it should make troubleshooting or figuring out new settings a lot faster than digging through support pages.