Agenta

You describe the job you want done, then hand the agent its context and tools. From there, you improve it through actual work and feedback rather than rewriting config from scratch. Product managers can get involved here without waiting on an engineer to wire up a prototype, which means the people closest to the problem can shape the agent directly instead of filing a ticket and hoping the intent survives translation. How much setup does it take? Less than you'd think. Just describe the job.

Prompts, skills, and tools are versioned like code, so every change is a revision you can inspect. If a new prompt hurts output quality, you roll back to the last good one instead of guessing. Big difference. This is the feature that turns prompt tinkering into something you can actually manage across a team.

You test a change against a test set before it goes live, using LLM-as-a-judge or evaluators you write yourself. The goal is catching quality drops before your users do. That matters most when a prompt change looks harmless in review but shifts outputs in ways nobody predicted, which happens more often than most teams expect once an agent has a dozen tools attached. Evaluations are unlimited on paid cloud plans, so you aren't rationing test runs.

Every run produces a trace that shows each step, what it cost, and where it failed. When an output goes wrong, you don't have to reproduce the whole flow blind. Just read the trace. No guesswork. Trace data retention stretches from one week on the free plan to three months on Business.

Agents can run on a schedule or fire when something happens in a connected app. Not bad. That moves an agent from a thing you manually poke to something that works in the background. Paid cloud plans include unlimited schedules and event triggers.

You can put a person in front of consequential actions so nothing irreversible runs on its own. Approval shows up as a normal part of the run rather than a separate tool bolted onto the side. Trust me, that detail saves a lot of awkward conversations when an agent decides on its own that refunding a customer is the right call. For teams automating anything that touches money or customer accounts, this is the difference between a demo and something you'd trust.

The open-source version runs on your own infrastructure under the MIT license, with unlimited users, projects, agents, and schedules. Your agents and data stay with you, which is often the deciding factor for regulated teams. Deploy with Docker Compose. Bring your own credentials.

The REST API is available at US, EU, and self-hosted base URLs, authenticated with an API key scoped to a single project, which means you can skip passing a project_id on every request, a small thing that adds up when you're making thousands of calls a day. A Python SDK installs with a single pip command. An OpenAPI 3.1 spec is published, so you can wire Agenta into your existing pipeline instead of clicking through the UI. No UI required.