steer

The Active Reliability Layer for AI Agents. Catch failures, teach fixes, and automate reliability

AI & Machine Learning Observability

Explore on GitHub Read Hacker News discussions

2Distinct people

10Mentions

0Explicit recommenders

135GitHub stars

REPOSITORY AT A GLANCE

What to know before exploring

What it is

The Active Reliability Layer for AI Agents. Catch failures, teach fixes, and automate reliability

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 4:01:40 AM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week10 dated mentions

Observed mentions: 2025-12-02 14:16:31 UTC to 2026-01-20 21:02:34 UTC. Weeks start Monday, UTC.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges

UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
Period	Start (UTC)	End (exclusive, UTC)	Mentions	Submissions	Comments
Dec 1	2025-12-01 00:00:00 UTC	2025-12-08 00:00:00 UTC	2	1	1
Dec 8	2025-12-08 00:00:00 UTC	2025-12-15 00:00:00 UTC	1	1	0
Dec 15	2025-12-15 00:00:00 UTC	2025-12-22 00:00:00 UTC	3	1	2
Dec 22	2025-12-22 00:00:00 UTC	2025-12-29 00:00:00 UTC	0	0	0
Dec 29	2025-12-29 00:00:00 UTC	2026-01-05 00:00:00 UTC	0	0	0
Jan 5	2026-01-05 00:00:00 UTC	2026-01-12 00:00:00 UTC	2	0	2
Jan 12	2026-01-12 00:00:00 UTC	2026-01-19 00:00:00 UTC	1	1	0
Jan 19	2026-01-19 00:00:00 UTC	2026-01-26 00:00:00 UTC	1	0	1

What Hacker News says

2 sources

steer_dev·Jan 8, 2026

AI Slop (apologies, filler) isn't just annoying; it is In-Band Signaling Noise. The model mixes control signals (persona) with data (payload). Prompting ("Be concise") is brittle. I switched to Shannon Entropy. The Hypothesis: Code/Data is mathematically "messy" (High Entropy). Slop is smooth and predictable (Low Entropy). I wrote a filter that blocks responses if entropy dips below ~3.5. The Payoff: It captures the blocked slop as a dataset for DPO. I use the math to gather data today so I can fine-tune a natively quiet model tomorrow. Repo: https://github.com/imtt-dev/steer

In Detecting "AI Slop" with Shannon Entropy (Python)

steer_dev·Jan 6, 2026

Doing post-mortems on my agent's failures over the holidays made me realize the problem isn't the model. It is the lack of a deterministic inference-time verification layer. I spent the break reading the recent Stanford/Harvard paper on agentic adaptation [1]. Their research provides mathematical proof for what I experienced in Q4: supervising only final outputs is a dead end. Agents learn to "ignore tools and improve likelihood," meaning they learn to lie more convincingly to pass evaluations while the underlying logic rots. I call this the Agent Lobotomy. The agent I have in production today is significantly dumber than the one I demoed in December. I was forced to strip autonomy, remove context, and add human checkpoints because I could not trust the probabilistic output. We are stuck in an Autonomy Retreat, creating an Authority Bottl

Read the rest

eneck [2] where agents are relegated to assistive tasks because the tail risk of autonomous action is too high. I built Steer (open source) to stop the bleed. In v0.4.0, I moved the architecture to an Agent Service Mesh pattern. Instead of decorating every function, you patch the framework (e.g. PydanticAI) at the entry point. It auto-discovers tools and enforces a reliability policy globally via deterministic Reality Locks. The real unlock is the data. By capturing the delta between a Blocked Response and a Taught Fix, Steer acts as a synthetic data factory for DPO. It moves reliability from a runtime tax to a training asset, allowing you to eventually refactor your prompt monolith into fine-tuned model weights. I've put together three cookbooks showing how this stops the lobotomy in SQL and RAG workflows: 1/ Framework Patching: https://github.com/imtt-dev/steer/blob/main/steer/cookbook/p... 2/ SQL Security Lock: https://github.com/imtt-dev/steer/blob/main/steer/cookbook/s... 3/ RAG Grounding Guard: https://github.com/imtt-dev/steer/blob/main/steer/cookbook/r... References: [1] https://arxiv.org/abs/2512.16301 [2] https://cloudedjudgement.substack.com/p/clouded-judgement-12...

In The Agent Lobotomy: Inference-time verification for autonomous systems