Nine Steps To Unbreakable AI Agents

Nine reasoning steps stand between a smooth AI agent and one that quietly hallucinates fake API docs to cover for a failed call. A Redditor going by u/blobxiaoyao dug into Google’s official Gemini agent design docs. The result: a single system prompt you can drop into any agent stack today. It forces the model to work through nine checks, covering dependencies, risk, hypotheses, and persistence, before it’s allowed to call a tool or answer you.

Why Agents Break in the First Place

The author points at four specific ways tool-calling agents fall apart in production:

• Premature Action Bias: the model fires off a tool call before it’s mapped out what needs to happen first.
• Fragile Error Handling: one bad API response and the agent either gives up or spams the same failing call in a loop.
• Risk Blindness: deleting a database row gets treated with the same caution as a harmless search.
• Premature Convergence: the model locks onto the first surface explanation instead of testing alternatives.

Anyone who’s built a multi-step agent has watched at least one of these happen live. The fix, according to this contributor, isn’t a better “think step by step” line. It’s a structured control flow.

How the Control Flow Works

The system prompt walks the model through nine ordered checks before it’s allowed to act:

1. Logical dependencies and order of operations
2. Calibrated risk assessment
3. Abductive reasoning, with multiple hypotheses ranked by likelihood
4. Adaptive plan updates when a hypothesis fails
5. Multi-source grounding across tools, policy, and conversation history
6. Precision quoting of the exact rule being applied
7. Completeness verification
8. Intelligent persistence: retry transient errors, change strategy on structural ones
9. Response inhibition, a hard stop that blocks output until steps 1-8 finish

Step 8 is the one worth sitting with. I got genuinely excited rereading it. It’s the fix most agent prompts skip! It draws a real line between a “please try again” throttling error and a broken request. Transient errors get retried up to a limit you set. Structural errors force the agent to change its arguments or approach, never repeat the same failed call. That single distinction kills the infinite-retry loop most agents fall into.

The Prompt Itself

This is the original poster’s exact system prompt, reproduced in full:

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}}

Two placeholders need filling in before you use it: `{{retry_limit}}` and `{{user_request}}`. Swap those for real values, or wire them into your prompt template so they populate automatically.

Where This Actually Helps 🎯

• Multi-step research agents that call several APIs in sequence and need to survive a rate limit without hallucinating results.
• Autonomous coding agents that touch a database or filesystem, where a wrong guess about “safe to delete” causes real damage.
• Customer-facing tool-calling bots that need to ask before an irreversible action but shouldn’t pester users over optional search parameters.
• Debugging assistants that keep proposing the same fix because they locked onto the first hypothesis.

One thing worth flagging: this isn’t built for single-turn Q&A or quick text rewrites. The nine-step overhead adds latency you don’t need for a simple answer. Save it for agentic loops with real tool calls and real consequences.

Prompt of the Day

Drop the full block above straight into your system instructions. Fill in `{{retry_limit}}` with something concrete, like “max 3 tries.” Then pipe `{{user_request}}` from your existing user input. That’s the whole setup, no extra scaffolding required.

If you’re building agents that touch real systems, test this against your worst failure case first. Pick the one where a tool call comes back wrong and watch whether it retries smart or just gives up. The original poster also built an interactive canvas to test and tweak the prompt live. It’s worth a look if you want to iterate on `retry_limit` and `user_request` without editing raw text every time. Check out the full breakdown and try it on your own agent stack.

I distilled Google’s official Gemini agent guidelines into a battle-tested agentic workflow system prompt
by u/blobxiaoyao in PromptEngineering