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AI

AI Agents

AI agents that earn their keep — autonomous systems for solo operators. Memory, orchestration, evaluation, and the boring infrastructure that makes agents reliable.

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Frequently asked questions

What is an AI agent?

An AI agent is an LLM-based system that takes actions on your behalf — sending an email, querying a database, or calling another agent — without you in the loop for every step.

How are agents different from chatbots?

Chatbots respond to messages. Agents execute multi-step plans, use tools, recover from errors, and persist context across sessions. The line is fuzzy; the test is whether the system runs autonomously between user messages.

What infrastructure does an agent need?

At minimum: an LLM provider with function calling, a memory backend, an action layer with audit trail, and a kill switch. Most production agents also need queues, retry policies, and observability.

Are AI agents safe to run autonomously?

Only if you've designed a kill switch, scoped permissions, and rate limits. The Salars stack treats agents as junior employees who need durable infrastructure — not as black boxes.

Can a solo operator build production agents?

Yes. The compute costs are bounded; the harder problem is operational discipline (monitoring, recovery, evaluation). Salars publishes the playbooks.