saurabhjain1592

4 ms·

1. 1.▲
   by saurabhjain1592 9mo ago

   Thanks for the thoughtful read. You’ve described exactly the maturity stage we’re targeting: past demos, dealing with retries, partial failures, side effects, and the need for real control once systems are live. On your questions: 1. Debugg
2. 2.▲
   by saurabhjain1592 9mo ago

   We intentionally require explicit tool and connector registration rather than doing blind network layer interception. The reason is deliberate: we want semantic understanding of what a call represents, not just raw HTTP payloads. When a con
3. 3.▲
   by saurabhjain1592 9mo ago

   This is a very accurate framing. We see the same failure mode in practice. You are right that enforcement is hard if you cannot first see what is actually happening at runtime. How we think about the observability to enforcement gap: In pra
4. 4.▲
   by saurabhjain1592 9mo ago

   Thanks for taking the time to write this. This is exactly the kind of critique I want. 1) Migration path gateway to proxy You are right that many enterprise projects start with perimeter checks and then want deeper execution control. We des
5. 5.▲
   by saurabhjain1592 9mo ago

   Good question. The overhead is designed to be low enough for inline enforcement. For the fast, rule based checks we typically see single digit millisecond evaluation time, and in gateway mode the end to end pre check usually adds around 10
6. 6.▲
   by saurabhjain1592 9mo ago

   Thanks. In practice, access control is enforced centrally by AxonFlow, not delegated to the orchestrator. Each LLM or tool call is evaluated at execution time against the active policy context, which includes the user, workflow, step, and t
7. 7.▲
   by saurabhjain1592 9mo ago

   Good question. By deterministic policy enforcement we mean rule-based checks that evaluate to an explicit allow or block decision at execution time. Today that includes a mix of regex-based checks (for example PII patterns), structured dete
8. 8.▲
   Show HN: AxonFlow, governing LLM and agent workflows

   11 points by saurabhjain1592 9mo ago|14 comments
9. 9.▲
   by saurabhjain1592 9mo ago

   Thanks, this is a great question. We intentionally avoid framing guardrails as “X percent confidence” checks on prompts or model output. In practice, probabilistic confidence at the text level has been the weakest place to enforce safety, e
10. 10.▲
    by saurabhjain1592 9mo ago

    Thanks, appreciate that. The intervention point ended up being more important than we initially expected. Once workflows become multi-step and stateful, the ability to pause, inspect, or halt execution based on context (not just inputs) bec
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    AxonFlow – a control plane for production LLM and agent workflows(github.com)

    9 points by saurabhjain1592 9mo ago|11 comments
12. 12.▲
    by saurabhjain1592 9mo ago

    Hi HN. When teams move AI agents from demos to production, the failures are rarely about model quality. They look a lot like classic distributed systems problems. - Long-running state across multiple steps. - Partial failures mid workflow.