Aura – üretim olaylarını araştıran ve düzelten bir Pas ajanı

We run a SaaS that handles petabytes of data. Our SRE team experimented with using claude, openclaw, langchain, etc. within our incident response workflows. We struggled with overflowing context, lethal trifecta vectors, hallucinations, and burned a lot of frontier tokens mostly on easy work. Approval fatigue was a challenge, and we drew a hard line at relaxing permissions in production. Long story short, we built and open-sourced AURA, a Rust-based harness specifically designed for the type of operations work which routinely involves large volumes of telemetry data and coordinated investigations across many domains of knowledge state. We have found even on open-weights models, root cause accuracy has been very good, and remediation actions are guarded with human-in-the-loop. AURA runs from a centralized configuration file where workers are defined and scoped to task domains, (e.g. logging review, metrics analysis, and git scm related queries). All permissions, tools access, LLM backend(s), worker prompts, and the main coordinator prompt are also defined in code. Permitted tool use is enforced deterministically outside the agent's context, so the agent cannot grant itself capabilities through prompting. AURA also handles the rest of the execution layer, such as human approval for sensitive actions and fault tolerance.

A lot of thought went into context window management: large tool outputs and worker responses are persisted to disk and agents are given tools to slice read as needed. The project is Apache 2.0. You can install rpm deb brew natively or pull the source and build your own. It runs either as an assistant on your workstation, or as a daemon that you can connect to via any OpenAI-compatible frontend. The GitHub repo is here: https: github.com mezmo aura The whole thing is free Free. No signups or open core trapdoor. Demo: We put together an 8-minute video showing a checkout outage resolved by following evidence from several different systems: https: www.youtube.com watch?v=TERHoRzT8cE tl;dw: 502s from a checkout service cause Grafana to trigger an incident via PagerDuty. AURA (using DeepSeek-V4-Flash) uses its workers and tools to correctly determine root cause as a memory leak in a downstream service, and attributes the incident to a defect in a recently merged PR.