Rasa

Rasa builds AI agents that extend large language models with structured flows, deterministic logic and built-in recovery patterns, positioning itself around trustworthy agents for real-world conversations rather than open-ended generation.

Constraining the model rather than relying on it is the whole architectural argument and it is the correct one for regulated or transactional use. A pure LLM agent asked to process a refund may explain the refund policy beautifully and then invent a step that does not exist, because nothing in its design distinguishes describing a process from executing one. Structured flows with deterministic logic mean the conversation can be fluent while the actions taken are constrained to what the business actually permits.

Rasa built-in recovery patterns are the part most platforms omit and the part that decides whether an agent survives contact with real users. Conversations go wrong constantly: users change their mind mid-sentence, answer a different question than the one asked, or interrupt with something unrelated. Handling that gracefully rather than restarting the flow is the difference between an agent that works in a demo and one that works on a phone line.

Rasa Enterprise RAG retrieves information in real time so answers stay fresh and verifiable against trusted data, which addresses the other failure mode where an agent answers confidently from stale training. Multilingual AI adapts to language, tone and context, orchestration coordinates agents and tools, and chat handles layered conversations with memory and adaptability. Customer support is the leading use case, resolving routine issues to leave people for the harder ones. Pricing is not published.