Related: I've been finding a lot of success (defined as more autonomy with less quality dropoff) with team-based workflows.
A lot of evidence for this.
- Adaptive Auto-Harness: Sustained Self-Improvement for Agentic System Deployment on Open-Ended Task Streams (https://arxiv.org/abs/2606.01770) talks about multi-agent, role-based adaptive harnesses collaborating on non-homogeneous workstreams
As noted by others, we already announced parts of /teamwork in Antigravity here: https://antigravity.google/blog/teamwork-when-ai-becomes-a-r... with more visualizations and more. Thi post was meant to report some advances in math/science/coding/engineering to show-case examples of tasks you can do with it.
We are constantly improving/evolving /teamwork and pushing those changes for external users. Will also post more about improvements and details for building teams of agents dynamically as Antigravity blog posts soon. Stay tuned!
Can you please let the product team know that there's space between Pro and Ultra pricing?
I can't afford/allow for ultra pricing for personal projects, but Pro runs through in about two days of casual usage for me. I've paid for extra credits, but my god are those not worth the price.
I would love to pay for a sub that's between Pro and Ultra.
Both take a long time. It used to be that antigravity was much faster than codex, but with recent releases it is substantially slower.
Inspecting the agents communication in teamwork, there seems to be an overwhelming amount of ceremony: BRIEF.md, specialized roles, multiple rounds of checking.
Is all that necessary? Codex also uses agent “swarms” but unless they’ve hidden how the sausage is made, it doesn’t seem so heavyweight.
Here is a short list of example results reported on the post; again we will have dedicated Antigravity posts for some of these.
* Math Open Problems & Formal Verification: Reporting 10+ open math and CS theory problems solved including 50M-line Lean 4 proof of Courtade–Kumar conjecture plus progress on several open problems in quantum query complexity, Tarjan's Deque Conjecture.
Also autonomous Lean formalizations of the Weil (1.8M lines), Birkhoff, and 3D Bochner–Riesz conjectures via Teamwork-LEAP, plus updates on Deep Review for STOC’27.
* Chemistry & Discovery: 95% top-1 exact match on NMR molecular structure elucidation (nearly 2× single-agent). Also empowering Nuclear Fusion and Quantum algorithm results as well autonomously generating a CausalAnalyst Benchmark. In addition, we show how /teamwork can start from Alphazero paper, implement a competitive go game, and autonomously improving it by playing with KataGo to get to the medium Elo level.
* Several Systems, TPU Kernel, & Infra:
* Clustering 1-Trillion-edge graphs on FarArray and RDMA (up to 180× faster),
* 800K-line Zircon & 170K-line libgav1 safe Rust rewrites (20–30% faster than C++/ASM),
* 4.09× JAXBench TPU kernel speedups with DuoDiscover,
* Google internal examples: surpassing 1 PiB fleet memory savings, and 24% fewer Borg TPU preemptions for Borg Scheduling.
FWIW, since Argon 4 was launched I used Antigravity (without the /boost or the /teamwork flags) for "playground research" and had some discoveries in cryptography[1] and speeding up Python, removing the GIL, improving the JIT, and optimizing it for certain libraries (e.g. NetworkX) [2]. I don't know how stable is the Python one but it passes the included tests. I have spent some GC budget on it though.
It is not clear to me how or if /teamwork is any different than Claude dynamic workflows.
Antigravity as a harness feels very behind. There's not even any auto mode. And they announced /goal long after I ever felt the need to use it in CC or Codex.
There is (--mode auto), but you can't access it without restarting the harness. Even then, you'll get plenty of mother-may-I showstoppers. I had to resort to using the --dangerously-skip-permissions option to get anything done. The good news is that Carbon is a pretty awesome model once you do get it working.
Seems likely that the people who maintain Antigravity don't use Antigravity to do it. It has too many rough edges that would be trivial to fix if someone actually noticed them.
The amount of breakthroughs this could deliver. Previously we should’ve established R&D departments, prepare multi-cultural teams, all to work on a single project. Today? I can see a domain expert, guiding antigravity and delivering fast.
I think the authors also used Antigravity to write this article, because it sucks at communicating whatever point it’s trying to make. I think it’s something like “We did the OpenAI 100 problems thing, but across many disciplines, and in collaboration with the experts instead of just dumping results on the world, and we also made the tooling feature available,” but I’m not sure because it has that distinct Gemini flavor of packing in so many low-level details that the actual point becomes entirely obscured.
Not just overly detailed, it misses important hints that would help the reader and puts several things into one sentence in a way that the sentence is difficult to follow:
> For n-bit integer multiplication, multi-agent campaigns simplified the theoretical Turing-machine bound below n log n, while a separate effort gave a new O(n log n) algorithm on a single two-dimensional grid whose C implementation matches the GNU Multiple Precision Arithmetic Library (GMP) bit-for-bit up to a billion bits.
Is "simplify a bound" a normal wording? What does it mean to simplify a bound?
It is a shame that Gemini prose has become too detail dense in recent releases. Its prose was one of its distinct advantages over Claude with that annoying Claude speak...
It seems like they already announced /teamwork earlier [0], but now just announced that they got some scientific results out of it. They probably intentionally want to keep this low key, but in that case it's not clear why announce it at all.
It sounds like they're saying they now offer an option, "/teamwork", that will throw more computer at a problem by choosing a team and a workflow that you the user don't have to (or get to?) know about.
I’m not sure if they are that far behind given the integration forced into every single one of their consumer products but yeah their developer LLM focus has been very disappointing.
I still don’t understand why they don’t use their massive market position to give more value to Google workspace customers to have more access to their models for teasing higher value plans. The existing credits timeout before they even build a playwright scraper.
Interesting that a Rust re-write pre optimisation was 30% slower than the C++ equivalent.
I wonder if they optimised the C++ it would actually have more speed benefits than the Rust alternative. Only they can't claim it's a Rust rewrite like every other company at the moment.
Related: I've been finding a lot of success (defined as more autonomy with less quality dropoff) with team-based workflows.
A lot of evidence for this.
- Adaptive Auto-Harness: Sustained Self-Improvement for Agentic System Deployment on Open-Ended Task Streams (https://arxiv.org/abs/2606.01770) talks about multi-agent, role-based adaptive harnesses collaborating on non-homogeneous workstreams
- team-solve (https://github.com/guregodevo/memdoor/tree/main/examples/wor...) team of small models beats Fable on LiveCodeBench
- Agensh paper (https://arxiv.org/html/2609.26781v1) also talks about "role-playing agents to study autonomous cooperation"
All show that role-based, multi-agent setups improve performance.
As noted by others, we already announced parts of /teamwork in Antigravity here: https://antigravity.google/blog/teamwork-when-ai-becomes-a-r... with more visualizations and more. Thi post was meant to report some advances in math/science/coding/engineering to show-case examples of tasks you can do with it.
We are constantly improving/evolving /teamwork and pushing those changes for external users. Will also post more about improvements and details for building teams of agents dynamically as Antigravity blog posts soon. Stay tuned!
Also I added a list of examples here: https://x.com/mirrokni/status/2121351383236436247?a=ma9UPZg_... and https://lnkd.in/p/esVTpR6p. I'll add a comment with examples as well.
Can you please let the product team know that there's space between Pro and Ultra pricing?
I can't afford/allow for ultra pricing for personal projects, but Pro runs through in about two days of casual usage for me. I've paid for extra credits, but my god are those not worth the price.
I would love to pay for a sub that's between Pro and Ultra.
Neither one has privacy (model training off) so it’s pretty much just a toy or for open source folks whose codebases get stolen by Google either way
When to use teamwork vs boost vs swarm?
Both take a long time. It used to be that antigravity was much faster than codex, but with recent releases it is substantially slower.
Inspecting the agents communication in teamwork, there seems to be an overwhelming amount of ceremony: BRIEF.md, specialized roles, multiple rounds of checking. Is all that necessary? Codex also uses agent “swarms” but unless they’ve hidden how the sausage is made, it doesn’t seem so heavyweight.
Here is a short list of example results reported on the post; again we will have dedicated Antigravity posts for some of these.
* Math Open Problems & Formal Verification: Reporting 10+ open math and CS theory problems solved including 50M-line Lean 4 proof of Courtade–Kumar conjecture plus progress on several open problems in quantum query complexity, Tarjan's Deque Conjecture.
* Chemistry & Discovery: 95% top-1 exact match on NMR molecular structure elucidation (nearly 2× single-agent). Also empowering Nuclear Fusion and Quantum algorithm results as well autonomously generating a CausalAnalyst Benchmark. In addition, we show how /teamwork can start from Alphazero paper, implement a competitive go game, and autonomously improving it by playing with KataGo to get to the medium Elo level.* Several Systems, TPU Kernel, & Infra: * Clustering 1-Trillion-edge graphs on FarArray and RDMA (up to 180× faster), * 800K-line Zircon & 170K-line libgav1 safe Rust rewrites (20–30% faster than C++/ASM), * 4.09× JAXBench TPU kernel speedups with DuoDiscover, * Google internal examples: surpassing 1 PiB fleet memory savings, and 24% fewer Borg TPU preemptions for Borg Scheduling.
FWIW, since Argon 4 was launched I used Antigravity (without the /boost or the /teamwork flags) for "playground research" and had some discoveries in cryptography[1] and speeding up Python, removing the GIL, improving the JIT, and optimizing it for certain libraries (e.g. NetworkX) [2]. I don't know how stable is the Python one but it passes the included tests. I have spent some GC budget on it though.
[1] https://arxiv.org/abs/2611.00512
[2] https://github.com/srw/cpython-experimental
Is CausalAnalyst the causal inference benchmark it talks about? Where are the results?
It is not clear to me how or if /teamwork is any different than Claude dynamic workflows.
Antigravity as a harness feels very behind. There's not even any auto mode. And they announced /goal long after I ever felt the need to use it in CC or Codex.
Auto mode has been there for few versions, and recently (1-2 days ago?) became the default
There is (--mode auto), but you can't access it without restarting the harness. Even then, you'll get plenty of mother-may-I showstoppers. I had to resort to using the --dangerously-skip-permissions option to get anything done. The good news is that Carbon is a pretty awesome model once you do get it working.
Seems likely that the people who maintain Antigravity don't use Antigravity to do it. It has too many rough edges that would be trivial to fix if someone actually noticed them.
I read changelog every release, and there's always a lot of work updating auto mode in last series (1.5.x)
The amount of breakthroughs this could deliver. Previously we should’ve established R&D departments, prepare multi-cultural teams, all to work on a single project. Today? I can see a domain expert, guiding antigravity and delivering fast.
This doesn't tell me how to use it.
I think the authors also used Antigravity to write this article, because it sucks at communicating whatever point it’s trying to make. I think it’s something like “We did the OpenAI 100 problems thing, but across many disciplines, and in collaboration with the experts instead of just dumping results on the world, and we also made the tooling feature available,” but I’m not sure because it has that distinct Gemini flavor of packing in so many low-level details that the actual point becomes entirely obscured.
Not just overly detailed, it misses important hints that would help the reader and puts several things into one sentence in a way that the sentence is difficult to follow:
> For n-bit integer multiplication, multi-agent campaigns simplified the theoretical Turing-machine bound below n log n, while a separate effort gave a new O(n log n) algorithm on a single two-dimensional grid whose C implementation matches the GNU Multiple Precision Arithmetic Library (GMP) bit-for-bit up to a billion bits.
Is "simplify a bound" a normal wording? What does it mean to simplify a bound?
It is a shame that Gemini prose has become too detail dense in recent releases. Its prose was one of its distinct advantages over Claude with that annoying Claude speak...
It seems like they already announced /teamwork earlier [0], but now just announced that they got some scientific results out of it. They probably intentionally want to keep this low key, but in that case it's not clear why announce it at all.
[0]: https://antigravity.google/blog/teamwork-when-ai-becomes-a-r...
I'm pretty sure they didn't because of the following nonsensical sentence and how generally disorganized the post was:
"In under eight days, the engine gained roughly 2,600 Elo, reaching 1,885 Elo, placing it between KataGo at 16 and 32 visits per move."
It sounds like they're saying they now offer an option, "/teamwork", that will throw more computer at a problem by choosing a team and a workflow that you the user don't have to (or get to?) know about.
> I think the authors also used Antigravity to write this article, because it sucks at communicating whatever point it’s trying to make.
The pattern seems to be pervasive at Google. Too much Ai, not enough human, top-down orders
Google is missing LLMs the way that Microsoft and Intel missed cell phones.
I’m not sure if they are that far behind given the integration forced into every single one of their consumer products but yeah their developer LLM focus has been very disappointing.
I still don’t understand why they don’t use their massive market position to give more value to Google workspace customers to have more access to their models for teasing higher value plans. The existing credits timeout before they even build a playwright scraper.
Interesting that a Rust re-write pre optimisation was 30% slower than the C++ equivalent.
I wonder if they optimised the C++ it would actually have more speed benefits than the Rust alternative. Only they can't claim it's a Rust rewrite like every other company at the moment.
I also thought of what speedup the tool could produce for the C++ original
Sloppification is now called "advancing". That may even be innovative.