Google’s Antigravity Teamwork framework is built for long-running research and engineering jobs, with groups of AI agents proposing, challenging, verifying, and refining each other’s work over hours or days. Google reports seven notable open-problem results from its Long Proof work, while a Gemini 3.7 Flash team separately built a cycle-level out-of-order RISC-V CPU simulator that booted xv6 to a shell and matched hardware timing within 0.71% on average.
Google let the agents keep working — and the jobs got ambitious
Google has been testing Gemini models in teams of autonomous agents, and the latest results are far beyond the usual “build me an app” demo.
With Antigravity Teamwork, groups of agents can work for hours or days on the same objective. They propose approaches, challenge one another’s results, verify intermediate work, and keep iterating until they have something ready for human review.
Google says that setup has now produced seven notable results across open problems in mathematics and theoretical computer science. In a separate systems-engineering test, a Teamwork configuration powered by Gemini 3.7 Flash built a cycle-level out-of-order RISC-V CPU simulator from scratch, booted xv6 to a working shell, and ran more than 100 standard RISC-V benchmarks.
That combination is what makes this update worth watching. Teamwork is being used on proofs, processor simulation, and real open-source performance work — three very different jobs, all built around the same idea: give several agents a shared objective and enough structure to keep improving the result.
Teamwork is more structured than “put several agents on it”
Google first introduced Teamwork as a multi-agent orchestration framework inside Antigravity. The current version is available through the `/teamwork-preview` command, and it does not use one fixed team for every problem.
Instead, Teamwork works with patterns. A pattern defines the roles the agents should play, how their work should move between them, and what has to be checked before the job can advance. Antigravity chooses a pattern based on the task and can change the number of agents while the run is underway.
The current set includes Iterative Coding, Distributed Coding, Long Proof, Self-Verification, and Document Review. A software project that splits cleanly into parallel work can use a different structure from a mathematical proof that needs repeated strategy search and verification.
That separation is important. The useful part is not simply having more model instances running at once. It is giving them a workflow that says who proposes, who checks, who combines, and when another round is worth running.
The seven research results come with an important model detail
The Long Proof pattern is where Google reports the seven notable open-problem results. It generates several candidate strategies, pairs them with agents that try to break or verify them, and then combines the strongest surviving ideas into a more complete approach.
The results span coreset construction for ℓp subspace approximation, sparse convex optimization, maximal inner-product embeddings, Hadamard quantization, the Erdős unit-distance problem, prefix-matrix factorizations, and Knuth’s Cycles Conjecture. Five resulting papers are available on arXiv.
There is one detail worth getting exactly right: Google says the seven results were obtained using Gemini 3.1 Pro. Three of them — problems 1, 3, and 4 in Google’s list — were also reproduced with Gemini 3.7 Flash. So this is not a claim that Flash alone produced all seven results.
For Knuth’s Cycles Conjecture, Google reports first proofs for two simpler constructions of the even-integer case, with proofs running beyond 40 and 70 pages. The shorter proof was formally verified in Lean. For the broader set, Google says human experts reviewed and confirmed the results, with Lean providing the formal verification path for the Knuth work.
Then Gemini 3.7 Flash built a CPU simulator
The hardware result is easier to picture.
Using Gemini 3.7 Flash, Teamwork built a highly accurate cycle-level simulator for an out-of-order RISC-V processor. It was not just executing RISC-V instructions functionally. The goal was to model the timing behavior of a modern out-of-order design closely enough to compare cycle counts against hardware ground truth.
The agents worked through two phases. First came functional correctness: building execution logic for pieces such as reorder buffers, caches, and the out-of-order pipeline, then getting the simulator to maintain correct architectural state and boot xv6. The second phase focused on timing, aligning the simulator’s behavior against strict reference measurements.
Google says the finished simulator booted xv6 all the way to a shell and successfully simulated more than 100 standard RISC-V benchmarks. On unseen test workloads, it reached an average cycle-alignment error of 0.71% against BOOM hardware execution ground truth.
That is a much richer engineering target than generating source code that compiles. The team had to build the simulator, run microbenchmarks, inspect traces, compare timing, and keep refining the microarchitecture until the behavior lined up with the reference.
The same framework also shipped real open-source optimizations
Google also pointed Teamwork at existing open-source software.
For Eigen, the widely used C++ linear-algebra library, the agents worked through microbenchmarks and identified an opportunity around matrix-vector operations where the matrix has a single row or column. The resulting implementation added a dedicated fast path using direct data access, SIMD operations, and four-way accumulator unrolling. Google says the change went through the normal open-source review process and landed upstream with assistance from Gemini 3.6 Flash.
A second project focused on ParlayHash, a high-performance concurrent hash table. Teamwork contributed to the design of “Swiss Parlay,” which brought ideas from Swiss Table into the library.
Google reports 2× throughput on initial inserts with 64 threads, 1.5× overall throughput with a single thread, and memory use per element that was 25% lower than the comparison sequential table while staying close to its performance. The improvement also landed upstream.
These examples give Teamwork a different kind of credibility from a closed benchmark. The work ended up inside existing projects after external maintainers reviewed it through their normal process.
The interesting part is the loop, not the agent count
Multi-agent systems are easy to describe as “several AIs working together,” but Teamwork’s design is more specific than that.
For Long Proof, candidate strategies compete before the system commits deeply to one route. Subproblems can be split into a dependency graph and solved in parallel where possible. Verification is built into the process, and later rounds can reuse useful results and observations from earlier work.
The coding patterns use a different rhythm. Distributed Coding can fan a decomposable engineering job across parallel workers and bring in critic review. Iterative Coding keeps a tighter agent-test-refine loop for work that needs repeated local improvement.
The result is a framework that changes shape around the problem. Mathematics, processor simulation, and library optimization do not need the same team structure, even if all three benefit from longer-running collaboration.
This is already moving into the Antigravity developer workflow
Teamwork is not only a Google research harness. The updated framework is being rolled into Antigravity through `/teamwork-preview` on paid plans, with Google saying more of the improvements will arrive over the next few weeks.
Gemini 3.7 Flash is a natural fit for that environment because Google is positioning it specifically around coding and agents. The model launched in August as Google’s latest Flash workhorse for software engineering, knowledge work, web development, and multi-step agent workflows.
That gives the research results a direct connection to a product developers can actually experiment with. The exact high-parallelism configurations behind some of Google’s research runs are not simply the default consumer setup, but the orchestration ideas are becoming part of the same Antigravity environment.
The broader direction is clear: the unit of work is getting larger. Instead of asking one model for one answer, developers can hand a structured team a problem and let the system keep working through planning, implementation, testing, critique, and verification.
The Upgrade Feeling
The strongest part of this story is not a single benchmark number.
It is the range of work produced by the same orchestration idea. One Teamwork pattern can spend hours exploring a proof. Another can build and calibrate a CPU simulator. Another can optimize production-grade C++ and send the result through an upstream review.
That makes agent orchestration feel less like a demo layer and more like an engineering primitive. The model still matters, but the structure around the model is starting to matter just as much: roles, verification, parallelism, shared state, and the ability to keep working long enough for a difficult problem to unfold.
For developers following the agent space, that is the signal here. The next jump may come from better models — but also from giving those models better ways to work together.
Qualcomm’s new Dragonwing Q-2390 and IQ-2390 processors are not built to run giant frontier models. Their AI accelerator is rated at just 1.1 TOPS. That is exactly why they are interesting. These chips combine modest on-device AI, machine vision, graphics, real-time control, deterministic networking and broad I/O in compact systems designed for kiosks, gateways, HMIs, PLCs, cameras, building automation and other devices that need to react locally. The industrial IQ-2390 adds ECC memory protection, a -30°C to 115°C operating range and long product-lifecycle support. The story is not about putting a chatbot in every machine. It is about pushing small, specialized AI into the control layer where latency, reliability, cost and connectivity matter more than model size.
This Is AI Hardware for Machines Nobody Calls AI Devices
When people hear “AI chip,” they usually imagine one of two things.
A giant accelerator inside a datacenter.
Or an NPU inside a premium phone or laptop.
Qualcomm’s Dragonwing Q-2390 and IQ-2390 target a less glamorous part of the market.
Industrial controllers.
Machine-vision systems.
Building-management equipment.
Retail terminals.
Gateways.
Kiosks.
Smart appliances.
PLC and CNC systems.
The chips are designed for devices that increasingly need to perceive, classify, decide and react locally without becoming expensive AI computers.
That makes them a useful signal of where edge AI may spread next.
Qualcomm Announced Both Chips Ahead of IFA 2026
Qualcomm introduced the Q-2390 and IQ-2390 on September 1, 2026.
The commercial Q-2390 joins the Dragonwing Q2 Series.
The industrial IQ-2390 becomes the first processor in the new Dragonwing IQ2 Series.
They share a common architectural idea.
Put application compute, graphics, machine vision, AI acceleration, a real-time microcontroller and extensive connectivity into one compact platform.
Then let device makers replace several separate chips and controllers with one more integrated design.
1.1 TOPS Sounds Tiny — and That Is the Point
Both product pages list a Qualcomm Hexagon NPU rated at 1.1 TOPS.
Compared with an 80 TOPS laptop NPU or a high-end datacenter accelerator, that number looks small.
It is.
These processors are not designed for local frontier language models.
They are aimed at smaller inference jobs.
Object detection.
Image classification.
Anomaly detection.
Sensor interpretation.
Simple predictive models.
Local automation decisions.
In many embedded systems, 1.1 TOPS can be useful if the workload is narrow and the software is optimized.
The target is practical intelligence per watt and per dollar, not benchmark supremacy.
The CPU Is a Four-Core Arm Design
Qualcomm’s detailed product pages list one Arm Cortex-A78 core and three Cortex-A55 cores running up to 1.9 GHz.
Qualcomm also markets the CPU under its Kryo branding.
That mix follows a familiar heterogeneous pattern.
The stronger A78 core can handle more demanding application work.
The A55 cores can run lighter tasks more efficiently.
For an industrial controller, that balance is more useful than simply maximizing core count.
The system may need to run Linux, a user interface, networking, device logic and AI inference at the same time.
The RISC-V MCU Is More Important Than It Looks
Both platforms include a real-time RISC-V microcontroller.
That detail separates industrial edge systems from ordinary consumer computing.
A language model or Linux application can tolerate variable timing.
A control loop often cannot.
A motor command may need to happen at a predictable interval.
A sensor must be sampled on schedule.
A safety state may need an immediate response.
The real-time MCU gives developers a deterministic execution resource alongside the larger application processor.
That allows AI and conventional real-time control to live inside the same platform without pretending they have identical timing requirements.
AI Should Not Sit Inside Every Control Loop
One of the biggest mistakes in physical AI is assuming a neural network should directly control everything.
Industrial systems are built around predictability.
A machine may use AI to classify an object or detect an abnormal vibration.
That result can then feed a deterministic state machine or control routine.
The AI contributes perception.
The real-time controller contributes timing and repeatability.
Dragonwing’s architecture reflects that separation.
The processor does not force every machine function through an AI model.
It gives designers several compute domains and lets them assign the right job to each.
Machine Vision Is a Natural Fit
Factories already use cameras for inspection.
Check whether a component is present.
Read a code.
Measure alignment.
Detect surface defects.
Confirm that a robotic process completed correctly.
Historically, some of that processing required a separate industrial PC or a specialized vision controller.
The Q-2390 and IQ-2390 combine camera support, an image signal processor, GPU resources and an NPU.
That creates a path toward smaller vision systems where more of the pipeline runs directly at the machine.
Local Vision Reduces the Need to Stream Everything to the Cloud
A factory camera can generate a large amount of data.
Sending every frame to a datacenter is expensive.
It adds latency.
It consumes network bandwidth.
It may create privacy or sovereignty concerns.
A local edge processor can analyze the stream near the camera.
Only an alert, classification, measurement or selected image needs to leave the device.
This is one of the simplest economic arguments for industrial edge AI.
Process the high-volume raw data locally.
Transmit the smaller result.
The IQ-2390 Is Built for Harsher Environments
The industrial IQ-2390 is not merely the commercial chip with a different name.
Qualcomm lists an operating range from -30°C to 115°C.
The platform uses industrial-grade packaging and includes ECC memory protection.
It is designed for systems exposed to heat, cold, vibration and shock.
Those requirements matter because factory and infrastructure equipment can operate for years in places where consumer electronics would never be expected to survive.
Reliability becomes a product feature, not a background specification.
ECC Memory Is a Quiet Industrial Requirement
ECC stands for error-correcting code.
Memory errors are rare.
In industrial systems expected to operate continuously for years, rare errors eventually matter.
ECC can detect and correct certain memory faults before they become corrupted data or unpredictable software behavior.
That is especially important when one platform combines AI inference, control logic, networking and machine vision.
A rugged AI edge system has to be more than fast.
It has to remain trustworthy over long operating periods.
Ten-Year Longevity Matters More Than Annual Chip Upgrades
Consumer devices are replaced quickly.
Industrial machines are not.
A factory controller may remain in service for a decade.
A building-management platform can stay installed even longer.
Changing the processor halfway through a product lifecycle can force expensive redesign and certification work.
Qualcomm says the IQ-2390 is designed around long product-lifecycle support, with current materials pointing to roughly a decade of availability and ecosystem support.
For industrial customers, that can be more important than receiving a faster chip next year.
Industrial AI Has a Different Upgrade Cycle
The AI industry moves quickly.
Factories move carefully.
That creates tension.
A model can improve every month.
Industrial hardware may be frozen for years.
The practical solution is not to replace every controller whenever a better model appears.
It is to design hardware with enough headroom, stable software support and standard frameworks so models can evolve within the existing platform.
Dragonwing’s value will depend heavily on whether Qualcomm and its ecosystem can sustain that software path over the promised product lifecycle.
Dual Gigabit Ethernet With TSN Is a Major Clue
The IQ-2390 includes two Gigabit Ethernet interfaces with Time-Sensitive Networking support.
TSN is designed to make Ethernet traffic more deterministic.
Ordinary Ethernet is excellent for moving data.
Industrial automation often needs more predictable timing.
A control packet arriving late can matter.
TSN adds mechanisms for scheduling and prioritizing time-critical traffic.
That means the same platform can participate in modern industrial networks while still handling AI and vision workloads locally.
Deterministic Networking Connects AI to Real Automation
An AI model can identify a defective part.
That information becomes useful only when another system can react.
Stop a conveyor.
Reject the item.
Alert an operator.
Adjust a process.
The network between perception and action therefore matters.
By including TSN-capable Ethernet, Qualcomm is targeting devices that sit inside real operational technology rather than simply collecting data at the edge.
The chip is meant to participate in the control environment.
The Q-2390 Targets Less Harsh Commercial Devices
The Q-2390 has a broader commercial and consumer focus.
Qualcomm lists applications such as retail point-of-sale systems, kiosks, access-control devices, smart appliances, smart agriculture, home robots, fitness equipment and enterprise terminals.
These products may need cameras, displays, connectivity and some local AI.
They generally do not need the same industrial temperature envelope or rugged lifecycle features as a factory controller.
The shared architecture lets Qualcomm cover both markets without treating them as identical.
The Q-2390M Adds Integrated Cellular
Qualcomm’s Q-2390 product page describes a Q-2390M variant with integrated LTE Cat 4 data connectivity.
That matters for systems installed where wired networking is inconvenient or unavailable.
A remote terminal.
A kiosk.
A field device.
A smart-agriculture controller.
Cellular connectivity can turn an edge processor into a self-contained connected product without requiring another modem subsystem.
That reduces board complexity and the number of components an OEM has to integrate.
The Platform Also Supports Wi-Fi, Bluetooth and Wired Expansion
The commercial Q-2390 supports a broad connectivity mix, including Wi-Fi, Bluetooth, dual Ethernet, PCIe, USB and serial interfaces.
The exact implementation can vary by product design and companion components.
That flexibility matters because IoT devices are rarely defined by one connection type.
A machine may use Ethernet for plant networking.
USB for peripherals.
PCIe for expansion.
Serial interfaces for legacy sensors.
Wireless connectivity for setup or remote management.
Integration is the product.
Reducing BOM Cost Is Part of the AI Strategy
Qualcomm repeatedly emphasizes bill of materials, board area and integration complexity.
That may sound like ordinary chip marketing.
It is actually central to edge AI adoption.
If adding AI requires a separate processor, vision chip, real-time controller, modem and networking device, the feature becomes expensive.
If one SoC can absorb several of those roles, AI becomes practical in lower-cost products.
The edge-AI market expands not only when models become smarter but when the hardware around them becomes cheaper to integrate.
A Smaller Board Can Create a New Product Category
Some machines have plenty of space for an industrial PC.
Others do not.
Door controllers.
Compact cameras.
HVAC systems.
Small HMIs.
Printers.
Scanners.
Embedded gateways.
A highly integrated processor lets developers add local intelligence where a large computer would be impractical.
That is why Qualcomm describes the IQ2 family around space-constrained, cost-constrained and power-constrained devices.
The AI opportunity exists precisely because the hardware cannot be large.
Android, Linux and Zephyr Cover Different Parts of the Stack
Qualcomm lists support for Android, Yocto Linux, Ubuntu and Zephyr.
Those operating systems serve different product styles.
Android can support rich touch interfaces.
Ubuntu can provide a familiar development environment.
Yocto gives embedded teams more control over a customized Linux image.
Zephyr targets resource-conscious real-time and embedded workloads.
Supporting several operating environments gives OEMs flexibility.
It also acknowledges that industrial AI is not one software stack.
The Hard Part Will Be Software, Not the NPU Number
A 1.1 TOPS NPU is useful only if the application can use it.
Developers need model-conversion tools.
Supported operators.
Runtime libraries.
Camera pipelines.
Drivers.
Debugging tools.
Board-support packages.
Long-term security updates.
The success of Q-2390 and IQ-2390 will therefore depend on the development ecosystem around the silicon.
Industrial companies do not buy TOPS.
They buy a path from prototype to a product they can ship and support.
Qualcomm Is Explicitly Selling the Path From Evaluation to Production
The IQ-2390 product page emphasizes BSPs, tools, evaluation hardware and documentation.
That is exactly what embedded teams need.
A chip can look impressive on a specification sheet and still fail commercially if integration takes too long.
Qualcomm is positioning Dragonwing around reducing that development burden.
The company says customer engagement has already begun through an early-access program.
Evaluation kits for both platforms are expected in early 2027.
Early Access Means These Are Not Yet Everywhere
The launch should not be interpreted as mass deployment across factories today.
Qualcomm says the Q-2390 and IQ-2390 are in early access.
Evaluation kits are expected to become available in early 2027.
Customers and module vendors can begin development before broad availability.
That makes September 2026 the start of the product cycle, not proof that industrial AI systems using these chips are already widespread.
The Industrial Edge Is Where Cloud Economics Change
Cloud AI works extremely well when a device has reliable bandwidth and latency is not critical.
Industrial systems often violate both assumptions.
Some factories isolate operational networks.
Some remote infrastructure has limited connectivity.
Some decisions have to happen in milliseconds.
Some video streams are too expensive to upload continuously.
Some data cannot leave the site.
Those constraints create a natural market for edge inference even when the local model is much smaller than a cloud model.
A Small Model Can Be More Valuable Than a Giant One
A factory does not need a frontier language model to decide whether a bottle cap is missing.
It may need a tiny vision model that runs reliably millions of times.
A building controller does not need a giant reasoning model to detect an abnormal sensor pattern.
It may need a compact anomaly detector that works offline.
AI value depends on the task.
The model that fits the machine, latency budget and power envelope can be more useful than the model with the largest parameter count.
Industrial AI Is Often Invisible to the User
A consumer notices an AI chatbot.
They may never notice an AI-enabled industrial controller.
The system simply works better.
A camera catches defects earlier.
A motor failure is predicted.
A building adjusts energy use.
A gateway filters sensor data before sending it upstream.
A machine reacts locally when connectivity disappears.
That invisible behavior may become one of the largest deployments of practical AI.
Not because the model is impressive to watch.
Because the system quietly makes better decisions.
Qualcomm Is Positioning Dragonwing as the Industrial Counterpart to Its Consumer AI Push
Qualcomm already sells AI-capable platforms into phones, PCs, automotive systems and higher-end industrial hardware.
Dragonwing extends the same edge-computing strategy downward into more constrained devices.
The company’s message is consistent.
Do more inference near the user or machine.
Reduce dependency on the cloud.
Combine connectivity and compute.
Use specialized accelerators for efficiency.
The Q-2390 and IQ-2390 show what that strategy looks like when cost and industrial reliability become the dominant constraints.
What Qualcomm Has Actually Confirmed
Qualcomm announced the Dragonwing Q-2390 and IQ-2390 on September 1, 2026.
Both platforms combine a quad-core CPU, Adreno 704 GPU, Hexagon AI acceleration and a real-time RISC-V microcontroller.
The detailed product pages list one Cortex-A78 core and three Cortex-A55 cores up to 1.9 GHz and an NPU rated at 1.1 TOPS.
The Q-2390 targets commercial, enterprise and consumer IoT devices.
The IQ-2390 targets industrial HMI, machine vision, gateways, PLC/CNC, building automation and related systems.
The IQ-2390 includes ECC memory protection, dual Gigabit Ethernet with TSN and an operating range from -30°C to 115°C.
Qualcomm is running an early-access program and expects evaluation kits in early 2027.
What We Should Not Claim
We should not say these processors run frontier language models.
Their NPU is rated at 1.1 TOPS and the target workloads are much smaller.
We should not say 1.1 TOPS guarantees a specific model speed.
Software, model architecture and memory all matter.
We should not say every Q-2390 product includes cellular connectivity.
Qualcomm specifically describes the Q-2390M variant as adding integrated LTE Cat 4.
We should not say the chips are already broadly shipping in finished industrial products.
They are currently in early access, with evaluation kits expected in early 2027.
And we should not say AI replaces deterministic industrial control.
The platform itself includes a real-time MCU because predictable control still matters.
The Bigger Shift Is AI Moving Into the Control Layer
The most visible AI systems live in datacenters and apps.
The next wave may be harder to see.
Inside a camera.
Inside an HMI.
Inside a building controller.
Inside a gateway.
Inside a factory machine.
Those devices do not need enormous models.
They need enough intelligence to perceive local conditions and act quickly without depending on a remote server.
Q-2390 and IQ-2390 are not exciting because they are powerful compared with an AI PC.
They are exciting because they are small enough, integrated enough and industrial enough to put AI in places where a big AI computer would never fit.
That is how intelligence becomes infrastructure.