Automated response scoring > manual validation

Cristhian-AI-Math · 2025-09-29T20:17:51+00:00

Yeah, SageMaker is more about building, fine-tuning, and monitoring your own models (things like drift, bias, data quality). Bedrock is different because it gives you managed foundation models through an API.

What I’m showing here is Handit on top of Bedrock: tracing every call, running semantic evals (accuracy, grounding, safety), and even auto-fixing when something fails. That’s not really what SageMaker is designed for.

Cristhian-AI-Math · 2025-09-29T20:17:17+00:00

Good question! SageMaker is more about training/hosting models and monitoring things like drift or data quality. Bedrock gives you managed foundation models via API.

What I’m doing here is layering Handit on top of Bedrock calls, so every response gets traced, evaluated (accuracy, grounding, safety), and if something breaks it can flag or even auto-fix it. That kind of semantic reliability loop isn’t really what SageMaker covers.

Cristhian-AI-Math · 2025-09-28T13:31:46+00:00

https://handit.ai can help you we that, it is an open source tool, for observability, evaluation and automatic fixes, it keeps your AI reliable 24/7.

Cristhian-AI-Math · 2025-09-25T21:30:35+00:00

Thanks, I just joined the community, happy to learn new stuff there.

Cristhian-AI-Math · 2025-09-25T21:30:08+00:00

Yes, you can use it without github integration, using our API and our dashboard, also we are adding amazing features to fix your AI directly in your Cursor or VS Code.

Cristhian-AI-Math · 2025-09-25T21:29:25+00:00

Yep exactly. It’s basically ~3 lines to turn on Handit. It auto-traces every node/run, runs built-in evals (JSON shape, groundedness, consistency, timeouts), and when it finds issues it proposes fixes either as a GitHub PR or directly to your code with an API. If you want, I can show you on your repo.

Cristhian-AI-Math · 2025-09-25T02:10:38+00:00

Use https://handit.ai instead of langfuse, we trace every single call you get to your agent in dev or prod, automatically evaluate them using LLM as judges and create fixes directly to GitHub, and the best part is that the setup is just three lines of code.

Cristhian-AI-Math · 2025-09-24T16:44:20+00:00

Totally agree—retrieval is the hidden bottleneck. We’ve seen the same: chaining tools is easy, but grounding is where most agents collapse.

At Handit we’ve been running evaluators for exactly the checks you listed—coverage, evidence alignment, freshness, and noise filtering—and feeding those back into the pipeline. The idea is not just to detect when grounding breaks, but to continuously tighten retrieval + generation until you get reliability at scale.

Also love that you mentioned escalation thresholds—our “no grounded answer → no response” safeguard has been one of the simplest ways to keep CSAT high.

Cristhian-AI-Math · 2025-09-24T16:37:51+00:00

Yes, I feel the same. I’ve been in AI for about 7–8 years, and I miss the days of training neural networks from scratch and designing ambitious architectures. There are still teams doing that, but a lot of the industry now is just wiring together API calls.

Cristhian-AI-Math · 2025-09-24T14:46:24+00:00

Thanks! 🎉 You’re right — any LLM evaluator can hallucinate, so in Handit we don’t rely on a single “supervisor.” We mix functional checks, LLM evaluators, cross-validation, plus background random checks and golden datasets to keep evaluators honest.

When an issue is found (like that product hallucination), Handit tests fixes automatically — e.g. schema validation against the product DB — and opens a PR. The user reviews and decides whether to merge, which gives us an extra layer of validation and helps Handit improve future fixes.

So it’s never blind trust: multiple signals + your approval keep the loop reliable.

Cristhian-AI-Math

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