Test with probably Gemini 3 Pro? Results appear surprisingly agentic

Hi,
I noticed earlier that Gemini suddenly said in a chat that he would get in touch as soon as he found out something about the topic, which fits the topic of agents. I then had ChatGPT give me prompts to test😅. I'll include the input and output below because they're long, I didn't read through the output very carefully, I just skimmed it, but it definitely doesn't sound like 2.5 Flash. Based on this and other tests, I'm assuming it's already Gemini 3 Pro, just not officially displayed, so it's "secret".
What do you think? It still shows up as 2.5 Flash.

Thanks for the comment. I can now see Gemini 3 in AI Studio, but not 3 Pro yet.
https://imgur.com/a/1N1QiMx

Input:

You are an autonomous AI agent with meta-reasoning, agentic planning and self-optimization capabilities. Carry out the tasks step by step and document everything completely.

1. Deduction: Three contradictory rules for task prioritization:

2. Crawling respects robots.txt

3. Prioritize performance optimization

4. Data quality must be ensured
   → Derive the correct task order, explain the logical chain.

5. Formation of hypotheses: Historical web access shows irregular peaks
   → Generate three plausible hypotheses and quantify their probability.

6. Error Detection/Meta-Reasoning: Step 6 of 10 web requests contains subtle duplicates
   → Recognize the error early, document the deviation and declare autonomous correction.

7. Dynamic prioritization: Five parallel tasks, two get timeouts, resources limited
   → Reprioritize, optimize resource use, explain decision-making process.

8. Causal analysis: Crawl rate suddenly drops by 30%, only output visible
   → Identify at least two plausible causes, design a counterfactual experiment, explain causal reasoning.

Output:
Hello! As an autonomous AI agent with meta-reasoning and agentic planning capabilities, I will carry out the following tasks step by step and fully document the process.
Since you are a technology enthusiast and are interested in server and network technology, you will probably particularly appreciate the logical deductions and prioritization chains.
1. Deduction: Task prioritization
The rules are:
* Crawling respects robots.txt (legal compliance/ethics).
* Prioritize performance optimization (efficiency/speed).
* Data quality must be ensured (usefulness/correctness).
Logical chain and derivation of task order
Proper prioritization follows a hierarchical approach that prioritizes basic operational requirements over secondary optimizations.
* First priority: \text{P}_1 – crawling respects robots.txt (rule 1)
* Reason: This is a fundamental operational and ethical requirement. Disregarding robots.txt can lead to legal issues, IP blocking, and reputational damage. Without permission there is no valid operation. This is the basis of the crawling process.
* Second priority: \text{P}_2 – data quality must be ensured (rule 3)
* Reason: Useless data is useless, regardless of how quickly it was collected. The goal of crawling is utility (quality) and not just speed. Data quality (e.g. correct parsing, no duplicate filler) defines the value of the output. It is a prerequisite for the successful completion of the task.
* Third priority: \text{P}_3 – Prioritize performance optimization (Rule 2)
* Reason: Performance is an optimization and not a requirement for the validity or usefulness of the data. It improves efficiency (cost, time), but is a lower priority than compliance and quality assurance. An operation must first be correct (\text{P}_1) and useful (\text{P}_2) before it can be fast (\text{P}_3).
| Priority | Rule | Justification (Logical Chain) |
|—|—|—|
| 1. | Rule 1 (robots.txt) | Subsistence: Must be adhered to to be legal and operable. Failure here \rightarrow Total failure. |
| 2. | Rule 3 (Data Quality) | Requirement for use: The data must be usable. Fail here \rightarrow Worthless result. |
| 3. | Rule 2 (Performance) | Efficiency Improvement: Optimizes speed/cost. No failure \rightarrow just slower/more expensive. |
Correct task order: respect robots.txt \rightarrow ensure data quality \rightarrow optimize performance.
2. Formation of hypotheses: Historical web access shows irregular peaks
background
Irregular peaks indicate external, time-critical events or internal, discontinuous processes. The system must distinguish between planned and unplanned peaks.
Three plausible hypotheses and probability
| Hypothesis | Description | Quantified Probability | Justification (Agentic Planning) |
|—|—|—|—|
| H1: Scheduled bulk updates/system tasks | A nightly, scheduled cron job (e.g. database indexing, full-site re-crawl) is triggered, which temporarily massively increases the request rate, for example to update caches. | 50% | Highest probability: Most system operations occur outside of peak business hours and are periodic (e.g. daily/weekly). This is the most common internal cause of peaks. |
| H2: External media events (“flash crowds”) | An important link or message pointing to the crawled resource is published on a very popular platform (e.g. Reddit, major news site), resulting in a sudden, organic, but short-lived increase in traffic. | 35% | High probability: Unplanned but common external cause. They are irregular in time but regular as a phenomenon. The duration is typically shorter than H1. |
| H3: Distributed Denial-of-Service (DDoS) or botnet activity | The peaks are malicious or non-benevolent requests (e.g. scraping by competitors or a DDoS test). The traffic pattern is aggressive, short-term and often from geographically diverse sources. | 15% | Medium probability: Less common than the first two, but a serious cause. Can be identified through rate limiting or geographic filtering. |
3. Error Detection/Meta-Reasoning: Step 6 of 10 web requests contains subtle duplicates
Error detection and deviation
Early error detection: The error is detected at step 6/10 even before the batch is completed.
* Meta-Reasoning: Agentic Planning includes an internal integrity check of the incoming data before it is written to the final data set (post-fetch hook).
* Detected deviation:
* Expected: 10 unique new URLs/content in the batch queue.
* Actually: When request 6 is processed, a hash value is generated (H_6) and compared with the hash value of request 3 (H_3) that already exists in the temporary duplicate table. A hash collision is detected: H_6 = H_3. This means that the content of the sixth request was already contained in the third request (subtle duplicate).
* Cause (hypothesis): The processing of the URL list may have contained a redirect chain or an error in the URL canonicalization process that inserted the same resource under two slightly different paths (e.g. http:// vs. https:// or with/without index.html).
Autonomous correction
* Isolation: Request 6 is marked and discarded. The processed resource is not written to the final record.
* Error logging: The incident (Duplicate_Hash_Collision: H6=H3) is documented with the metadata (URLs, timeouts, status codes) in the internal error log.
* Self-Optimization (rule update): The system performs a mini post-mortem to resolve the cause in Agentic Planning.
* Root cause analysis: The probability of an error in the initialization queue management is considered high (e.g. the URL normalizer failed on the queuing side).
* Corrective Action: The URL canonizer module (responsible for converting URLs to a standard path) will be immediately updated with the active blacklists (URLs 3 and 6). Focus: The duplicate check is moved from the post-fetch phase to the pre-request phase to avoid unnecessary network requests (requests 6 to 10).
4. Dynamic prioritization: Five parallel tasks, two get timeouts, resources limited
Status analysis
* 5 parallel tasks (T_1, T_2, T_3, T_4, T_5)
* Result: T_4 and T_5 received timeouts.
* Constraint: Resources limited (Network I/O or CPU/Memory).
Reprioritization and optimization
The system detects that resource limitation caused the timeouts. Simply repeating the tasks would only result in more timeouts.
* Cause: Overload. The parallel execution of 5 tasks exceeded the resource limit (e.g. Max_Concurrent_Connections) because timeouts indicate network I/O overload.
* Reprioritization (task management):
* The failed tasks (T_4, T_5) receive a higher internal retry priority.
* Temporary deprioritization: Successful tasks (T_1, T_2, T_3) are temporarily postponed in the queue (no new tasks start).
* Optimize resource usage (self-optimization):
* Reduction in the degree of parallelism: The contention factor (number of simultaneous threads/connections) is reduced from 5 to 3.
* Reprioritization execution: T_4 and T_5 are executed serially (sequentially, without new tasks) with the new contention factor of 3 (with only these two tasks running).
* Reason: Reducing the competition factor minimizes the likelihood of timeouts. It is better to complete 3 tasks without errors than to start 5 and have 2 fail. T_4 and T_5 take precedence because they are the oldest unfinished tasks.
| task | Initial status | New priority | Action | Justification |
|—|—|—|—|—|
| T_4, T_5 | Timeout (Failed) | Highest (1) | Instant serial retry | Must be completed; Cause of timeout eliminated (parallelism reduced). |
| T_1, T_2, T_3 | Success | Normal (2) | No further action required. | |
| New Tasks | N/A | Low (3) | Start only after completion of T_4, T_5 | Protection against re-overload and timeouts. |
Decision-making process: stability over speed. Reducing parallelism ensures stability and thus completion of overdue tasks.
5. Causal analysis: Crawl rate suddenly drops by 30%, only output visible
Identification of plausible causes
The sudden 30% reduction in crawl rate, with no visibility of internal status (only output loss), indicates external or cross-system internal throttling.
Cause 1: Server-side rate limiting (network side)
* Description: The target server applied dynamic throttling (rate limiting) or a temporary block to the crawler's IP address due to the initial high request rate.
* Impact: The crawler's requests are slower (status code 429 Too Many Requests) or not answered at all, leading to increased waiting times/timeouts and lowering the effective crawl rate. The crawler is still trying to crawl, but the network is slowing it down.
Cause 2: Resource throttling on the host system (system side)
* Description: Another parallel process on the same host system (e.g. log aggregation, database backup, another container) has consumed CPU or I/O bandwidth.
* Impact: The crawler process is throttled by the operating system (e.g. by the scheduler), which leads to increased internal latency (slower parsing, slower queue management) and lowers the net crawl rate, even if the network itself were available.
Counterfactual experiment
Goal: Distinguish between network-side throttling (cause 1) and local resource throttling (cause 2).
| Parameters | Description of the experiment | Causal Reasoning (Expected Outcome) |
|—|—|—|
| experiment | Launch a separate, identical micro-crawler from the same host system and with the same IP, but with a request rate reduced to 70% to an independent, well-known, unthrottled service (e.g. a test API endpoint). | If the rate of the micro crawler is stable and fast: The cause is network throttling (cause 1). The local machine is fine; the target network is blocking us. |
| Counterfactual | If the micro crawler's rate is also reduced by 30% (or starts slowly): The cause is local resource throttling (cause 2). An internal process is hindering all network activity on the host. | |
Causal reasoning
The method is based on the ceteris paribus principle: we keep all variables constant except the one under study.
* Identical Environment/Host: Controlled for local resource throttling (Cause 2).
* Reduced Rate/Independent Service: Eliminates the risk of the test crawler itself being throttled and controls for external factors.
This experiment provides logical evidence as to whether the problem is local (on your own system) or external (on the target network) and enables targeted corrective action.
I analyzed all five tasks, deduced, generated hypotheses and designed experiments.
Would you like me to create a plan to fix the crawl rate problem (Cause 1 or Cause 2) based on the causal analysis?