Autonomy

Autonomy is the only complete platform-as-a-service for distributed agentic AI systems of every scale.

[Autonomy @autonomy_comp]

https://t.co/UMrvgP4gJu

[Autonomy @autonomy_comp]

https://t.co/bW7U0A5qcS

[1Password @1Password]

Agent swarms are incredibly powerful and dangerously easy to deploy unsafely with today’s security models.

[1Password @1Password]

If you want agent-powered products that can run safely in real production environments, this model is worth digging into 🔗 https://t.co/5VV4nu7wQw

[1Password @1Password]

We worked with @autonomy_comp to show a better pattern with 1Password: • just-in-time access • least privilege by default • no standing creds • no hardcoded secrets

[Jayne Ingram @seejaynecode]

@mrinal @autonomy_comp @claudeai 15 minutes to a live app. Meanwhile I'm still debating with myself whether my folder structure is 'scalable enough.

[Mrinal Wadhwa @mrinal]

Gokul is spot on in this post. But the challenge is even bigger.

The last gen of vertical AI companies are not just competing against one deep-working long-horizon agent. They are competing against parallel fleets of them.

Autonomy enables their competition to create parent agents that can spawn and delegate work to thousands of sub-agents. Each sub-agent has its own filesystem, a shell to run CLI tools, and the ability to write and run new programs on the fly.

They divide complex problems, attack from multiple angles, and converge on outcomes in a fraction of the time.

Agents, in @autonomy_comp, are modeled as concurrent actors that automatically form secure distributed clusters to enable massive scale on a tiny infra footprint. This creates orders of magnitude advantages in costs, speed, and scale.

The question to benchmark is: Can your specialized agent outperform a coordinated team of 100s or 1000s of really-cheap general-purpose agents that can code their way around problems in real-time?

If not, then the time to change your approach is now.

[Mrinal Wadhwa @mrinal]

How product teams collab with customers has fundamentally changed.

On a call with a @Box customer, they wished videos uploaded to a Box folder could be automatically transcribed. These videos are often in different languages so they also wanted all the transcripts to be translated to english and then relevant info from those transcripts logged to the right systems of record.

30 minutes later we had vibe-coded and shipped to Autonomy a live first version of a product with AI agents that did exactly what the customer wanted.

To build a scalable video transcription product, you need to handle large file uploads, manage socket connections for real-time progress, integrate speech-to-text and language models, wrangle rate-limits, etc.

A first version, in the past, would've taken months. Scaling to many users would require orchestrating complex pipelines, balancing load across model providers, connection pools, failure handling and more.

With @autonomy_comp and @claudeai code, months are compressed into 30 minutes without needing to build any of the infra. If you want to build something like this yourself, a link to a full guide and a live demo of the app is in the comments below.

This ability to rapidly prototype AI agents and autonomous products is magical in the hands of product leaders, forward deployed engineers, and solution consulting teams.

I'm amazed by what customer calls look like now!

[Mrinal Wadhwa @mrinal]

Presented a 2-minute talk on Autonomy at the AI for Good Summit in SF yesterday.

2 minutes. No pressure, right? 😅
How'd I do?

[Mrinal Wadhwa @mrinal]

Claude Cowork + Autonomy is lovable 🥰

Vibe-coded in Cowork and shipped with Autonomy:
An app that uses parallel deep research agents to fact-check news articles.

It took 15 minutes and the app was live on a public address in @autonomy_comp

Great work @claudeai @felixrieseberg 👏