Autonomy
12 posts
Autonomy
@autonomy_comp
Autonomy is the only complete platform-as-a-service for distributed agentic AI systems of every scale.
San Francisco Se unió Ağustos 2025
17 Siguiendo11 Seguidores
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If you want agent-powered products that can run safely in real production environments, this model is worth digging into 🔗 bit.ly/4qfB5Yw
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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
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@mrinal @autonomy_comp @claudeai 15 minutes to a live app. Meanwhile I'm still debating with myself whether my folder structure is 'scalable enough.
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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.
Gokul Rajaram@gokulr
VERTICAL AI CHALLENGE Vertical AI Founders: You've spent 2+ years building your agents, training your model on your customers' data, embedding into workflows, creating a powerful GTM motion, all the best practices. You've beaten back challengers and are the #1 or #2 player in your vertical. I'm sorry, you cannot relax. In fact, you need to massively up your game. Turns out you are facing an existential challenge: long-horizon agents (eg: Claude Code). Agents that are not trained on a specific domain, but can reliably work for hours or days on end in pursuit of a goal, self-correct, and actually do stuff. I'm sure many Vertical AI founders will say: "Oh, we are not worried. We are the system of record for decision traces. We train on enterprise-specific context. That's why these horizontal agents can never catch up with this." You might well be right. But, but, but ... you cannot afford to bury your head in the sand. These long-horizon agents will get better very, very quickly. You need to understand precisely how good they are at the exact jobs you've built your agents on. You cannot wait for someone else to do this. For example, if you're a legal AI company with an agent that automates contract review, you must compare how good your specialized agent is versus a general-purpose long-horizon agent that's simply given the contract and asked to perform the same review. My challenge to you: Assign a strong engineer on your team to focus 100% on using long-horizon agents (with minimal context, other than just the contract in the example above) to compete with your custom-trained agents. Benchmark how the long-horizon agents perform vs your agent. Rinse and repeat it every few months. Like with most other things worth measuring, what matters is the rate of improvement (the "slope" vs the Y-intercept). If the long-horizon agent is 30% as good as your vertical agent on Day 1, but 50% as good on Day 60, and 70% as good on Day 120, you need to reassess your product strategy. AGI is coming for everyone. Long-horizon agents are the closest we have to AGI, and as a Vertical AI company, you need to figure out how you compete and survive. Game on.
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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!
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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?
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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 👏
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