Build AI agents that actually work like humans.
The fastest way to build, test and deploy autonomous AI agents that automate your workflows.
What process shall we automate?
Build me an agent that enriches new leads with company research and posts a brief to Slack.
Channels
Opus 4.8Instructions
Three ways to build your AI agent
BuildingBuild with chat
Tell Talona what you need in plain language and it assembles the right skills and tools for the job.
Opus 4.8Build by hand
Refine what chat drafted or build it from scratch in the visual builder, where every field is yours to set.
> Build me an outbound SDR agent
● talona ▸ create_agent
⎿ Dan, the Sales guy
● talona ▸ add_skill
⎿ lead-research
● talona ▸ connect_integration
⎿ HubSpot
● talona ▸ deploy
⎿ live on Slack
Build with MCP
Use Talona's MCP from Claude Code or any AI copilot to build and deploy agents without leaving your editor.
Deployable to channels you already use
Top high-intent accounts
- Northwind Labs120 staff · viewed pricing 4×
- Brightwave60 staff · invited 5 teammates
- Tessel & Co90 staff · booked a demo
Drafted 3 intro emails to the top accounts. Sending them needs your approval.
Why builders choose Talona
A persistent agent with its own memory and history that it reviews and keeps current on its own.
The managed agent keeps server-side session state, but there is no self-maintaining memory.
On its own isolated machine with a persistent disk, so memory and files survive every redeploy.
A managed cloud sandbox per session, tied to Anthropic's stack.
Any model from any provider, or bring your own key. No lock-in to one vendor.
Claude models only. No routing to another provider.
Yes. One agent for everyone, with every turn attributed to the person who asked and their own logins used.
No single shared team agent. Each person runs their own.
It runs code and drives a real browser, with its own persistent files. One gateway connects over 100 tools.
Capable code and browser tools, but engineers assemble and host the setup.
Approval gates on risky actions and one audit log of every tool call, tied to the person who asked. On by default.
Permissions per tool, but a person-level audit log lives in Enterprise Claude.
One credit covers everything an agent does. No seats to count.
Per-seat subscriptions plus token and runtime fees.
Talona vs Relevance AI
- One stateful teammate, or stateless runs? Talona: A persistent agent with its own memory and history that it reviews and keeps current on its own. Relevance AI: Agents keep long-term memory, but it does not curate itself.
- Where does each agent run? Talona: On its own isolated machine with a persistent disk, so memory and files survive every redeploy. Relevance AI: A shared multi-tenant cloud. No isolated machine per agent.
- Which models can it use? Talona: Any model from any provider, or bring your own key. No lock-in to one vendor. Relevance AI: A curated set of managed providers, with your own key on paid plans.
- Can the whole team share one agent? Talona: Yes. One agent for everyone, with every turn attributed to the person who asked and their own logins used. Relevance AI: Shared agents and per-person logins, but attribution and roles are Enterprise features still rolling out.
- What can the agent actually do? Talona: It runs code and drives a real browser, with its own persistent files. One gateway connects over 100 tools. Relevance AI: Runs Python and a browser tool, but the agent has no filesystem of its own.
- Who's accountable for what it does? Talona: Approval gates on risky actions and one audit log of every tool call, tied to the person who asked. On by default. Relevance AI: Approvals are built in, but the attributed audit log and roles are Enterprise tier.
- How does billing work? Talona: One credit covers everything an agent does. No seats to count. Relevance AI: Seats plus two separate usage meters to track.
Talona vs Gumloop
- One stateful teammate, or stateless runs? Talona: A persistent agent with its own memory and history that it reviews and keeps current on its own. Gumloop: Stateless by default. Each run starts a fresh conversation with no built-in memory.
- Where does each agent run? Talona: On its own isolated machine with a persistent disk, so memory and files survive every redeploy. Gumloop: Code runs in a per-agent sandbox, but the agent itself holds no lasting state.
- Which models can it use? Talona: Any model from any provider, or bring your own key. No lock-in to one vendor. Gumloop: A broad model menu with your own key, within their catalog.
- Can the whole team share one agent? Talona: Yes. One agent for everyone, with every turn attributed to the person who asked and their own logins used. Gumloop: Team workspaces and per-person credentials, with the attributed audit log on Enterprise.
- What can the agent actually do? Talona: It runs code and drives a real browser, with its own persistent files. One gateway connects over 100 tools. Gumloop: Runs code and scrapes the web, built to assemble flows rather than an agent with its own files.
- Who's accountable for what it does? Talona: Approval gates on risky actions and one audit log of every tool call, tied to the person who asked. On by default. Gumloop: Human approvals are built in, with the attributed audit log reserved for Enterprise.
- How does billing work? Talona: One credit covers everything an agent does. No seats to count. Gumloop: Credit-based, metered per node and per run.
Talona vs Claude Managed Agents
- One stateful teammate, or stateless runs? Talona: A persistent agent with its own memory and history that it reviews and keeps current on its own. Claude Managed Agents: The managed agent keeps server-side session state, but there is no self-maintaining memory.
- Where does each agent run? Talona: On its own isolated machine with a persistent disk, so memory and files survive every redeploy. Claude Managed Agents: A managed cloud sandbox per session, tied to Anthropic's stack.
- Which models can it use? Talona: Any model from any provider, or bring your own key. No lock-in to one vendor. Claude Managed Agents: Claude models only. No routing to another provider.
- Can the whole team share one agent? Talona: Yes. One agent for everyone, with every turn attributed to the person who asked and their own logins used. Claude Managed Agents: No single shared team agent. Each person runs their own.
- What can the agent actually do? Talona: It runs code and drives a real browser, with its own persistent files. One gateway connects over 100 tools. Claude Managed Agents: Capable code and browser tools, but engineers assemble and host the setup.
- Who's accountable for what it does? Talona: Approval gates on risky actions and one audit log of every tool call, tied to the person who asked. On by default. Claude Managed Agents: Permissions per tool, but a person-level audit log lives in Enterprise Claude.
- How does billing work? Talona: One credit covers everything an agent does. No seats to count. Claude Managed Agents: Per-seat subscriptions plus token and runtime fees.
Talona vs GPT Workspace Agents
- One stateful teammate, or stateless runs? Talona: A persistent agent with its own memory and history that it reviews and keeps current on its own. GPT Workspace Agents: Workspace Agents keep memory across sessions, but you do not own how it is curated.
- Where does each agent run? Talona: On its own isolated machine with a persistent disk, so memory and files survive every redeploy. GPT Workspace Agents: An ephemeral sandbox that is torn down when the task ends.
- Which models can it use? Talona: Any model from any provider, or bring your own key. No lock-in to one vendor. GPT Workspace Agents: OpenAI models only. No routing to another provider.
- Can the whole team share one agent? Talona: Yes. One agent for everyone, with every turn attributed to the person who asked and their own logins used. GPT Workspace Agents: Shared in the workspace, but per-person attribution sits in the Enterprise audit log.
- What can the agent actually do? Talona: It runs code and drives a real browser, with its own persistent files. One gateway connects over 100 tools. GPT Workspace Agents: Code and a browser in a sandbox, with tools you connect inside ChatGPT.
- Who's accountable for what it does? Talona: Approval gates on risky actions and one audit log of every tool call, tied to the person who asked. On by default. GPT Workspace Agents: Admin controls and a compliance log, gated to Business and Enterprise.
- How does billing work? Talona: One credit covers everything an agent does. No seats to count. GPT Workspace Agents: Per-seat plans plus usage on top.
Connect to 100+ integrations
Frequently asked questions.
Talona is a no-code platform for building your own AI coworkers. You describe the role you want filled or a task you want automated in plain English, and Talona assembles a production-grade agent with the right skills and access, showing you every choice before it ships. The result is an AI coworker that works for you 24/7.
Tools like n8n and Gumloop have you wire up a flowchart of nodes and branches that runs the same fixed path every time. Talona builds a real agent instead. It reasons about each task and decides which tools to use in the moment, so it adapts to open-ended work a fixed graph can't anticipate.
No. You build agents by describing what you want, and Talona handles the configuration and deployment underneath. Developers can go deeper when they want finer control, but it isn't required to get an agent running.
Anything from a Slack triage agent that watches your channels to a compliance reviewer that flags risky content before it ships. If you can describe the job in plain English, Talona can usually assemble an agent for it.
Talona ships a catalog of integrations you connect with one click, including Slack, Notion, Linear, and Google Workspace. Connecting takes a single sign-in step, and your tokens stay encrypted on our side so the model never sees them.
Ship the same agent native to Slack, Microsoft Teams, WhatsApp, Telegram, or the web app. Your team uses the channel they already open every morning, and the agent behaves the same everywhere.
Yes. Mark any action as approval-required and the agent pauses mid-task, waiting for a person to approve or deny before it acts. Every tool an agent runs is also recorded in a trace with secrets redacted, so you can review exactly what happened.
Yes. Every agent runs on its own isolated machine, and connected-account credentials stay behind a gateway the model can never read. Secrets are encrypted before they reach the database, and our security page covers the full picture.
Both work. Invite teammates into a shared workspace where each person's role decides what they're allowed to do. Or send a personal link to a friend so they can use the agent without joining your workspace. The same agent powers both sides.
You can start for free with one deployed agent, then move to a paid plan as your usage grows. Pricing runs on credits, a single unit that covers everything an agent does, so one number tells you what you've used.