How to Build Autonomous AI Agents with Hyperagent

TL;DR

Hyperagent creates autonomous agents with their own cloud computers, browsers, skills, memory, and scheduled workflows. A user can describe a job in plain English, connect tools such as Slack, and let agents monitor competitors, manage inboxes, create content, or pass work to one another, while reserving sensitive decisions for human approval.

Transcript

Most people think an AI agent is just a chatbot that runs a script. Mine has its own computer and tracks my competitors without me writing full breakdowns as it goes. And 5 minutes ago, none of that existed. But an agent I set up last week built the whole thing while I got a cup of coffee. So now, you're probably assuming I'm running one of the big... Read More

Key Insights

• Hyperagent gives every agent its own cloud computer and browser, enabling it to visit competitor pages, inspect current information, use enabled tools, and complete work in the world instead of relying only on knowledge contained in a model’s training data.
• Agent creation starts with a plain-English job description rather than a manually written system prompt. Hyperagent asks follow-up questions, generates the agent configuration, and exposes its identity, model, tools, skills, and knowledge in separate tabs that the user can inspect or change.
• The market-watching agent evaluates competitive developments rather than merely reporting raw changes. In the demonstration, it compares two competitors, checks their pricing pages, weighs their moves against the user’s strengths and prices, and identifies which development deserves greater attention.
• Skills are reusable instructions that encode how the user performs a task or makes a judgment. Scout receives criteria for evaluating threats, reading pricing pages, distinguishing launches from quiet tests, and comparing competitive moves with the user’s positioning, and those skills can be shared with other agents.
• Memory prevents the agent from treating every session as a fresh start. With automatic saving enabled, Scout can retain useful patterns, important pages, and prior observations, while the user controls each memory’s type, importance, and whether it belongs to one agent or is shared.
• Live mode allows an agent to run on a schedule and report through Slack without waiting for a new prompt. The user chooses the model, run destination, channel, and frequency, then instructs Scout to investigate meaningful changes while remaining silent when nothing important has happened.
• Agent collaboration allows one specialized agent to hand work directly to another. A market watcher can identify a significant competitor move, pass it to a content agent, and initiate a drafted response, leaving the user to review and approve the final version rather than coordinate every intermediate step.
• Human approval is retained for work involving judgment, tone, or potentially sensitive replies. Routine issues can be handled automatically, while consequential actions are posted for review and wait for confirmation, and a command center lets the user score agent performance against a custom rubric.
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Questions & Answers

Q: How do you build an autonomous agent with Hyperagent?

Start a new Hyperagent work session and describe the job in a few plain-English sentences. Hyperagent asks questions to fill important gaps, such as which companies the agent should monitor and what changes justify an alert. It then creates the agent’s system prompt and configuration. You can review its identity, select a model, enable tools, add skills and knowledge, and test the resulting agent.

Q: What can a Hyperagent market-watching agent do?

A market-watching agent can visit competitor websites with its built-in browser, inspect pricing pages, compare multiple companies, and decide whether a change matters to the user’s business. The demonstrated agent, Scout, weighs developments against the user’s strengths, positioning, and prices. It can then explain the threat, recommend responses, link the supporting page, or remain silent when nothing meaningful has changed.

Q: How do skills improve a Hyperagent’s decisions?

Skills encode a repeatable method that the agent can apply whenever it performs relevant work. The user teaches Scout which questions to ask, what qualifies as a threat, what should be ignored, and how competitive developments should be evaluated against the user’s positioning. Skills can be stacked and shared across agents, allowing multiple agents to apply the same standards without receiving the full guidance again.

Q: How does memory work in Hyperagent?

Memory stores useful information that an agent discovers while working, so later sessions do not begin from scratch. With automatic saving enabled, Scout can remember patterns such as which competitor tends to reveal launches on a particular day or which pricing page deserves attention. Users can also add memories manually and assign their type, importance, and visibility to one agent or every agent.

Q: How can a Hyperagent run automatically in Slack?

Live mode is configured under invocations and can connect the agent to Slack. After authorizing a workspace, the user gives the agent an identity, adds it to selected channels, chooses its model and reporting destination, and sets how often it runs. Team members can mention the agent in a channel, while scheduled monitoring can post findings only when the configured significance threshold is met.

Q: How does Hyperagent avoid sending too many alerts?

The agent can be instructed to report only when a meaningful development occurs and to say nothing when its checks find no important change. Scout applies the user’s threat criteria, market knowledge, and accumulated memory before deciding whether to post. When it does report, the Slack thread can show its reasoning, the pages it inspected, and the steps it followed, allowing the user to verify the conclusion.

Q: How can multiple Hyperagents collaborate as a team?

Each agent can specialize in a different responsibility while retaining its own computer, skills, and memory. The setup described includes a market watcher, an inbox agent, and a content agent. Agents can pass work directly to one another, so a significant competitive development can trigger a drafted response without the user manually relaying instructions, while the user remains responsible for final approval.

Q: When should a human approve an AI agent’s work?

Human approval should remain in the workflow when a task involves judgment, tone, or a response that could be received badly. In the demonstrated setup, agents handle small and repetitive matters independently, but sensitive changes and customer-facing replies are posted for review and wait for confirmation. This keeps the routine majority automated while preserving direct human control over consequential decisions.

Summary & Key Takeaways

• Hyperagent turns a short job description into an agent with its own cloud computer, system prompt, model, tools, and browser. The demonstrated market-watching agent checks competitor pages, compares changes against the user’s positioning, and explains which developments matter instead of merely returning raw website differences.

• Reusable skills teach agents how the user evaluates situations, while memory preserves important discoveries across sessions. Live mode adds scheduled, autonomous operation and can deliver reports through Slack. The market watcher is configured to remain silent when nothing important changes, reducing the stream of low-value alerts common to conventional monitoring tools.

• Multiple specialized agents can operate together as a team. The example combines market monitoring, inbox triage, and content creation, with agents able to hand tasks to one another. Human approval remains required for judgment-sensitive actions, while rubrics and feedback loops help improve routine output over time without repeatedly rewriting prompts.