Autonoma

Inspiration

We were inspired by a simple idea: companies are not just collections of software tools, but living systems made up of roles, decisions, handoffs, and workflows. Most AI products today act like isolated assistants that help with one task at a time. We wanted to go further and build something that behaves more like an actual organization.

That led to Autonoma: a zero-employee, agentic business orchestration platform where specialized AI agents can be assembled into a working company instance. Each agent has a role, a set of responsibilities, and the ability to coordinate with other agents to move work forward. AutoHDR pushed this vision even further for us. It showed us how powerful specialized models can be when they are treated like focused departments rather than generic chatbots. That inspired us to make visual intelligence a first-class part of the organization, not a separate app.

What it does

Autonoma lets a user create an AI-native company instance that can automate internal and external business workflows across communication, operations, planning, and execution. Instead of prompting a single model repeatedly, users define a business and Autonoma spins up a coordinated team of role-based agents such as CEO, operations, marketing, finance, support, and project managers.

These agents can collaborate on tasks, delegate work, share context, and execute multi-step workflows with less manual intervention. The platform is designed to support different AI model providers and different models per agent, so the business can use the best model for each responsibility rather than forcing every task through one provider.

We also built a Vision Department powered by AutoHDR. This gives every company instance access to image-based capabilities like grouping, ranking, and visual analysis. In our real estate flow, that means a company can automatically process property photos, organize image sets by view or angle, and support downstream content generation and listing workflows. More broadly, it turns AutoHDR into a reusable visual intelligence layer that can support any business where image understanding matters.

How we built it

We built Autonoma as a modular orchestration system with adapter support for multiple AI providers, so agents are not tied to a single model stack. One of our key design goals was to make provider choice part of the system architecture. We focused on making it possible to assign different providers and models to different agents, while using Gemini as the default provider for newly created company instances. We also explored how Featherless AI could fit into this architecture as a routing layer for model and provider selection.

On the product side, we focused heavily on company instance creation. The platform already had a more manual, step-by-step setup flow, but we wanted to make the experience much more accessible. We started designing an LLM-powered layer that can turn unstructured user intent into a structured organization setup. That means a user should be able to describe their business in plain language and have Autonoma generate an initial company structure, agent roles, and workflow recommendations, while still preserving structured decision points for critical setup choices.

We also extended the system conceptually with a Vision Department for AutoHDR integration. In particular, when the company creation prompt indicates a real-estate business, Autonoma can route visual tasks through AutoHDR to support ad imagery workflows and photo intelligence tasks. The result is a company instance that is not only text-native, but multimodal and domain-aware.

Challenges we ran into

One of the biggest challenges was balancing flexibility with structure. It is easy to let users describe a business in open-ended language, but much harder to reliably convert that into a clean, constrained, and useful company configuration. We had to think carefully about how much freedom to give the LLM layer versus how much of the existing structured setup should remain in place.

Another challenge was orchestration itself. Once multiple agents are involved, problems like synchronization, coordination, and shared context become much more important. We had to think beyond prompting and into systems concerns like multithreading, locks, barriers, and other synchronization mechanisms so that agents can work concurrently without stepping on each other.

A third challenge was making the platform truly provider-agnostic in practice. Supporting multiple model providers is not just a configuration problem; it affects agent capabilities, routing logic, defaults, and failure handling. That became even more relevant as we thought about how to integrate a smart router like Featherless AI into the existing adapter system.

Finally, integrating AutoHDR in a way that felt native to the platform rather than bolted on was important. We did not want visual intelligence to feel like a demo-only feature. We wanted it to function as a reusable department that fits the same orchestration model as every other agent role.

Accomplishments that we're proud of

We are proud that Autonoma is not just another wrapper around a single model. It treats a business as an orchestrated system of specialized agents, which is a much more realistic way to automate real work.

We are also proud of the architectural direction around model flexibility. Building the platform with adapter support for multiple providers creates a strong foundation for assigning the right model to the right job, and for making Gemini the default experience while still keeping the system extensible.

Another accomplishment is the Vision Department concept. Integrating AutoHDR as shared visual infrastructure makes the platform more than a text-based automation tool. It gives businesses a reusable capability for image understanding and opens the door to industry-specific workflows, especially in real estate.

We are also proud of the company instance creation direction. The move from a rigid multi-step setup into a smarter unstructured-to-structured flow makes the platform much more approachable for users who understand their business but do not know how to design an agent system from scratch.

What we learned

We learned that agentic systems become significantly more useful when they are organized around roles and responsibilities instead of raw model access. Users do not think in terms of “which model should I prompt next”; they think in terms of “who in my company should handle this.”

We also learned that multimodal intelligence becomes much more valuable when it is embedded inside a workflow rather than presented as a standalone feature. AutoHDR is powerful on its own, but it becomes more compelling when it acts like a department inside a business that other agents can call into when needed.

Another lesson was that onboarding is everything. The power of an agentic company means very little if setting one up is too complicated. That is why the unstructured-to-structured company creation layer became such an important focus for us.

Finally, we learned that orchestration introduces real systems engineering concerns. Coordination, shared memory, model routing, and provider abstraction are not side issues; they are central to making multi-agent products reliable.

What's next for Autonoma

The next step is to make company instance creation nearly one-shot. We want a user to describe their business, upload relevant company materials like files or PDFs, and have Autonoma generate a structured organization with role definitions, workflows, and model assignments automatically, while still allowing human review of key decisions.

We also want to deepen provider and routing support by expanding the adapter layer, making Gemini the default for new instances, and exploring Featherless AI as an intelligent routing layer for models and providers. That would let each department in the company use the best available model for its task.

On the systems side, we want to implement stronger multi-agent concurrency and synchronization primitives so agents can operate in parallel more safely and efficiently. On the product side, we want to improve context management, including ideas around dependency graphs, knowledge persistence, and integrations inspired by tools like Obsidian and Nexus.

For the demo and real-world rollout, we want to sharpen the real estate use case: if a user creates a real-estate company instance, Autonoma should automatically activate AutoHDR-backed visual workflows for listing imagery, ad generation, grouping, and downstream marketing automation. Longer term, we want to port the project cleanly to GitHub, deploy it live, improve the frontend, and turn Autonoma into the default way small and medium-sized businesses stand up an AI-native organization.