This session introduces Dana, a local-first agent programming language designed for building AI agents. Get a working expert agent in minutes. Features include long running, multi-step agent workflows on a single line; built-in concurrency for parallel LLM calls with zero async keywords; and deterministic execution with learning loops to improve reliability over time. Ideal for sensitive data, air-gapped environments, or cloud API limitations.

This session introduces Dana, a local-first agent programming language designed for building AI agents. Get a working expert agent in minutes:

• Long running, multi-step agent workflows on a single line: step1 | step2 | [step3a, step3b, step3c] | step4
• Built-in concurrency for parallel LLM calls with zero async keywords
• Deterministic execution with learning loops that improve reliability over time

Whether you're dealing with sensitive data, air-gapped requirements, or cloud API limitations—come see what agent development looks like when everything runs locally.

This session introduces Dana, a local-first agent programming language designed for building AI agents. Learn how to build expert AI agents locally with long running, multi-step workflows on a single line, built-in concurrency for parallel LLM calls with zero async keywords, and deterministic execution with learning loops that improve reliability over time.

This session introduces Dana, a local-first agent programming language designed for building AI agents. Get a working expert agent in minutes:

• Long running, multi-step agent workflows on a single line: step1 | step2 | [step3a, step3b, step3c] | step4
• Built-in concurrency for parallel LLM calls with zero async keywords
• Deterministic execution with learning loops that improve reliability over time

Whether you're dealing with sensitive data, air-gapped requirements, or cloud API limitations—come see what agent development looks like when everything runs locally.

This session introduces Dana, a local-first agent programming language designed for building AI agents. Get a working expert agent in minutes:

• Long running, multi-step agent workflows on a single line: step1 | step2 | [step3a, step3b, step3c] | step4
• Built-in concurrency for parallel LLM calls with zero async keywords
• Deterministic execution with learning loops that improve reliability over time

Whether you're dealing with sensitive data, air-gapped requirements, or cloud API limitations—come see what agent development looks like when everything runs locally.

This session introduces Dana, a local-first agent programming language designed for building AI agents. Get a working expert agent in minutes: long running, multi-step agent workflows on a single line (step1 | step2 | [step3a, step3b, step3c] | step4), built-in concurrency for parallel LLM calls with zero async keywords, and deterministic execution with learning loops that improve reliability over time. Whether you're dealing with sensitive data, air-gapped requirements, or cloud API limitations—come see what agent development looks like when everything runs locally.

This session introduces Dana, a local-first agent programming language designed for building AI agents. Get a working expert agent in minutes:\n- Long running, multi-step agent workflows on a single line: step1 | step2 | [step3a, step3b, step3c] | step4\n- Built-in concurrency for parallel LLM calls with zero async keywords\n- Deterministic execution with learning loops that improve reliability over time\n\nWhether you're dealing with sensitive data, air-gapped requirements, or cloud API limitations—come see what agent development looks like when everything runs locally.