Solid

Solid gives AI agents their own computers, accounts, and budgets so that long-running, complex jobs can be handed over and finished without the user needing to supervise every step. You bring the goal and the ground rules; the agents work out the steps, check the result, and report back. The product is described as being built for complex, long-running work for you and your team, rather than being a personal assistant: agents can build apps, automate workflows, and tackle work you lack the time or expertise for. They pick and set up their own real machines, create accounts, and pay for services, and they use your apps either through APIs or by logging in like a person. Close your laptop, and Solid owns the job from start to finish. The context Solid addresses is the gap between a chat window and a completed piece of work. Conventional assistants stop when the conversation closes, and many agent products depend on prebuilt connectors, which means they only work with tools that someone has already integrated. Solid's premise is that the agent should instead handle the setup and the troubleshooting itself. As the site puts it, agents connect to any tool the job needs, build what is missing, and check the result, with no prebuilt connectors required. That matters because the real friction in delegating work is rarely the initial request — it is the tool access, the account sign-ups, the failed steps, and the loose ends that a person would otherwise have to chase. Solid is designed so that users describe a job in plain language and receive finished work they can review, ready to use or share. The first autonomy area is self-sufficiency. Solid's agents choose the tools and handle the setup, including for software you have never used, on the stated principle that if a person can use it, they can too. They operate their own devices, which the site lists as Windows and macOS computers, Linux servers, iPhones, and Android phones. They maintain their own accounts, such as Google and Apple accounts, and they can sign up for and pay for services within the budget and approval rules you define. Because they do not need a prebuilt connector, you are not limited to a connector list: agents can connect through an API, build a missing integration, or operate a website, desktop application, or phone app directly. This means a job can proceed even when the right tool was never officially integrated. Two further autonomy areas cover failure and learning. Self-healing: if a tool fails or their setup breaks, agents can investigate, make a repair, and check that the job runs again. When they need help, they explain what is blocking progress rather than silently stalling, and users can also talk to a real person on the Solid team. Self-improving: agents keep the fixes that worked and learn from team corrections, and those lessons change how they use tools and approach future jobs. Solid states that agents remember your team's instructions and feedback across jobs, so when you correct how something is done, they keep that lesson and apply it the next time it is relevant. The practical effect is that explanations do not have to be repeated every time, and the agent's approach to shared context improves over time. The fourth autonomy area is self-scaling. Agents can bring in help and manage it: they create more Solid agents or bring in agents such as Codex and Claude Code, divide the work across any tools the job needs, coordinate the team, and return one checked result. For users this means a large job does not have to be decomposed and managed by hand. The agent acts as the coordinator, parceling out portions of the work and collecting the outcomes into a single verified deliverable that a person can review. In one of the site's illustrations, a Solid agent gathers results from other agents working across business tools and hands one checked result to a person, which is the intended pattern for larger workloads. Overall, Solid works as a meta-agent: it can see and manage its own workspace within the access you give it, so you can ask what is running, why it is needed, or how much a job cost at any time, and it can break down the AI usage, machines, and purchases used for the job. The workflow begins with a plain-language description of the job; for example, asking for a dashboard update triggers a sequence in which the agent connects to Gmail and HubSpot, researches on LinkedIn, builds the dashboard, deploys it, and verifies the data. Agents keep working after you close your laptop, then message you with the result, ready to use or share, and they will ask when they need a decision. Boundaries are set by you: you choose what agents can access and which actions require approval, so you might let them research and draft while requiring approval before sending a message, buying a service, or deploying a change. The outcomes described are about delegation with control. Users can hand over work that they lack the time or expertise to do, while retaining oversight through access controls, budgets, approval rules, and the ability to review work along the way. Because agents check their own results and revise from feedback, and because they explain what is blocking progress when they cannot proceed, the user is not required to monitor every step. Because they remember team instructions, the cost of briefing does not have to be paid again for each job. And because the whole monthly payment becomes one balance for AI usage, machines, and purchases, there is no separate platform fee layered on top, which keeps cost accountability inside the budget and approval rules the user defines. Solid publishes a set of example workflows. In sales demos, agents turn customer meeting notes into a tested demo and return the hosted link in your conversation, following steps from reading the notes and mapping the buyer's workflow to building with sample data, hosting and testing the app, and returning the link and test results. In LinkedIn lead generation, agents research accounts using your ideal customer profile, show the evidence, draft outreach for approval, follow up on approved messages, book qualified meetings, and update the CRM. In AI product evaluation, agents build eval scenarios and success criteria, run them after each release or change, simulate users completing real tasks, judge outcomes against expected behavior, and report what passed, failed, and why. In production bug resolution, agents investigate the alert, reproduce the issue, assess impact, write and test a fix, open a pull request for engineer approval, and verify recovery after deployment. In customer support, agents read a stalled ticket, gather the full customer history, find the cause across systems, apply the fix within your policies, and confirm and record the resolution. A customer service resolution workflow is also listed among the finished-job examples. Solid says builders and operators at companies including Revolut, ElevenLabs, EY, British Airways, Stanford, Berkeley, NVIDIA, MIT, Swiggy, NYU, and the Government Digital Service use it, and the product is aimed at individuals and teams with complex, long-running work. Pricing has three monthly subscriptions: Starter at $40 per month for getting started with a focused task, a simple app, or a small workflow; Pro at $160 per month for regular work, active app building, and more room to test and iterate; and Max at $640 per month for heavier workloads, larger apps, and several projects running at once. The full monthly payment becomes one balance for AI usage, machines, and purchases your agents make, with no extra platform fee, and a trial starts with $20 on Solid. For developers, the Solid API lets you deploy always-on agents inside your product or as the product, keeping context, working across approved systems, building missing pieces, and carrying long-running jobs under the controls you set. For enterprises, you can set access, budgets, policies, and approvals across your workspace and run on Solid Cloud, in your own VPC, or on-premises, with availability depending on your setup. Taken together, Solid's proposition is that agents should own a job end to end rather than assist inside a single conversation. By giving agents their own machines, accounts, and budgets — and by adding self-healing, self-improvement, and the ability to scale with additional agents — Solid targets work that is too long-running and too multi-step for a chat-based assistant. The control layer is what makes the delegation practical: you set budgets, access, policies, and approvals, agents report what they used and what they need, and you get finished work delivered back to you.