From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents
arXiv:2607.08028v1 Announce Type: cross Abstract: Enterprise large language model (LLM) applications often begin as prototypes whose behavior is carried by prompts and retrieval context…
Read the full articleYou might also wanna read
Study Reveals "Constraint Decay" in LLM Agents for Backend Code Generation Under Structural Requirements
Large Language Model (LLM) agents demonstrate strong performance in autonomous code generation under loose specifications. However, producti
arxiv.org·1mo ago
Prompt Injection Explained as a Role Confusion Problem in LLMs
LLMs can't tell who's speaking. We show they identify roles by writing style, not tags, and exploit this with CoT Forgery, injecting fake re
role-confusion.github.io·11d ago
From Prompt Engineering to Context Engineering: Evolving LLM Inference Approaches
As our use of LLMs has changed from conversational chatbots and into integral decision-making components of complex systems, our inference a
chrisloy.dev·8mo ago
Token Consumption Analysis in LLM-Based Multi-Agent Software Engineering Systems
LLM-based Multi-Agent (LLM-MA) systems are increasingly applied to automate complex software engineering tasks such as requirements engineer
arxiv.org·1mo ago
Building a Production Control Layer for Reliable LLM Structured Outputs
Most LLM failures in production aren’t random — they’re predictable. I kept hitting broken JSON, silent failures, and outages that froze my
towardsdatascience.com·24d ago
Meta-harness optimization loop on Islo: A 200-line POC for automated LLM agent improvement
A 200-line POC that wires the meta-harness optimization loop onto Islo sandboxes. Goes from 0/5 to 5/5 in four proposer steps.
zozo123.github.io·2mo ago
Comments
Sign in to join the conversation.
No comments yet. Be the first.