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AI Agents
AI agents that earn their keep — autonomous systems for solo operators. Memory, orchestration, evaluation, and the boring infrastructure that makes agents reliable.
Articles in this cluster
- advanced ai agent capabilities
- agent training performance
- ai agent applications
- ai agent basics
- ai agent benchmarks
- ai agent business implementation
- ai agent development implementation
- ai agent memory
- ai agent orchestration
- ai agent tool use
- learning capabilities
- security challenges
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.
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