Talona is a platform for building production-ready AI agents without writing code. You describe what the agent should do in plain English, the platform assembles the right skills and integrations from a curated catalog, and the agent deploys to its own dedicated runtime in under 75 seconds. Today we are opening Talona to everyone.
AI agents have moved from experiment to expectation. Teams want assistants that read their docs, watch their inboxes, and ship without weeks of glue code. Most of the existing tools force a tradeoff: a visual workflow builder that struggles with conversation, a developer SDK that needs code on every change, or a managed service that locks you into one vendor's stack. Talona is the platform we wanted to use ourselves: production-grade infrastructure, no code required, and no lock-in to a single channel or model.
What you can build with Talona
The product is general-purpose, but four patterns cover most of what users build in their first month:
- Customer support agents that answer questions from your docs and ticket history.
- Research assistants that browse the web and return briefed summaries with citations.
- Internal ops agents that handle routine team requests on Slack or Telegram.
- Scheduled jobs that run on a cron and post results back to a channel.
Every agent ships with web chat by default. Adding more channels takes about two clicks each.
How Talona is different
Four design choices set Talona apart from other agent tools.
Conversational, not workflow-first
Most agent tools are workflow builders dressed up: you wire nodes together, the result is a graph that runs end to end on a trigger. Talona inverts the model. You build a conversational agent that holds memory, takes initiative, and operates over time. Workflows are still possible (any task can be scheduled), but they are not the primary unit of composition. Conversations are.
A dedicated runtime per agent
Every Talona agent runs on its own VM with its own IP and its own filesystem. There are no noisy neighbors, no shared process to crash, no per-tenant resource caps. Idle agents suspend to zero compute, so the per-agent cost works out to single-digit cents per month at typical workloads.
Multi-channel out of the box
The same agent can be wired to web chat, Slack, Telegram, email, and other channels with two clicks each. Memory and integrations are shared across channels, so a teammate who taught the agent something on Slack does not need to re-teach it on email.
Observability built in, not bolted on
Every agent has a live trace viewer that shows every model decision, every tool call, every result. Failures bubble up to the run header without filtering. The median time to diagnose a failed run on Talona is under a minute, which is the difference between an agent that ships and one that gets quietly turned off.
Talona vs Gumloop
Gumloop is a visual workflow builder for AI automation. You drag nodes onto a canvas, wire them together, and the result is a graph that executes on a schedule or a trigger. It is a strong choice for repeatable, predictable automations like a Monday-morning summary of last week's reviews.
Talona is conversational and stateful. You build an agent that holds memory, can be talked to over chat channels, takes initiative across long horizons, and adapts its behavior based on instructions you give it in natural language. The mental model is different: workflows execute, agents collaborate.
- Pick Gumloop when you have a clearly-defined, deterministic workflow and the value is in not having to write the glue code. The shape of the work is known in advance.
- Pick Talona when you want a teammate or a stakeholder to be able to ask the agent something, get an answer, and have the agent do real work in response. The shape of the work emerges over time.
Talona vs Claude Managed Agents (Anthropic Agent SDK)
Anthropic's Claude Agent SDK is a developer toolkit for building agents in code. You write Python or TypeScript that uses the SDK to manage state, call tools, and orchestrate runs. The platform-level concerns (deployment, scaling, observability, channel integrations) are entirely your responsibility.
Talona is a complete platform. The model, the runtime, the deployment, the integrations, the observability, the channels are all included. There is no code required to ship an agent. The tradeoff: you build with the catalog of skills, tools, and integrations that Talona supports, rather than wiring up arbitrary Python.
- Pick the Claude Agent SDK when you have an engineering team and a custom integration that no platform supports out of the box. You want maximum control and you can absorb the cost of building deployment yourself.
- Pick Talona when you want an agent shipped this afternoon by anyone on your team, not just your engineers. The catalog of skills and integrations covers what you need, and you would rather invest your engineering time elsewhere.
Talona vs building agents from scratch with LangGraph
LangGraph is an open-source Python framework for building stateful agent graphs. It gives you primitives for nodes, edges, state, and execution. Like the Claude Agent SDK, it puts the platform concerns on you: you decide where the agent runs, how it scales, how you observe it, how it integrates with chat channels.
Talona handles all of those concerns. The cost is that you cannot define arbitrary graph topologies in code. The benefit is that an agent that would take a week to build, deploy, and observe with LangGraph takes under five minutes on Talona.
- Pick LangGraph when you are an ML engineer building a research agent with novel control flow. You need full programmability of the agent loop and you accept that you will build the platform around it.
- Pick Talona when the agent's job is well-defined enough that the value is in the integrations, the channels, the memory, and the observability rather than the graph topology. Most production agents are in this category.
How to start
Talona has a free tier that is enough to build, deploy, and use an agent end to end. The fastest path to a running agent is the five-minute walkthrough: Build your first AI agent in five minutes. For tier details, see the pricing page.
If you have a specific use case in mind, the agent builder will walk you through the right combination of skills and integrations. If you would rather see what is possible first, browse the catalog of pre-built integrations and skills inside the dashboard.