Tag
#reliability
Reports tagged reliability.
Reports
11 reportsOct 2026
10-12 multi-agent-systems How AI agents turn separate efforts into an accepted result See how useful AI teams connect responsibilities, evidence and acceptance—and judge their value by what one worker’s contribution lets another do and whether the resulting work survives the right checks. Sep 2026
09-20 agent-harness How an AI Agent Should Decide Which Decisions Deserve Credit or Blame A final score does not explain a trajectory. Reliable improvement reconstructs the decision, tests fair alternatives, measures uncertainty, and applies the smallest reversible repair. 09-20 finance What a market view must contain before a financial agent builds a position A decision-ready market view is an accepted forecast packet: one resolvable claim, bound to the information available when it was made, passed through explicit stop rules, and handed to sizing without a hidden trade instruction. 09-19 agent-harness When an AI agent’s past becomes useful An agent learns from experience only when past outcomes change later choices through scoped, testable, revisable guidance that survives variation without hidden regressions or disproportionate cost. 09-13 agent-harness When an Agent Fails, Should You Change the Model or the System? The useful distinction is not weak intelligence versus bad engineering. It is which intervention removes the failures that matter—and whether the gain survives realistic costs and checks. 09-13 agent-harness When can an AI agent say the task is complete? Completion is a judgment about the requested result—not a synonym for stopping, passing a check, or sounding confident. 09-02 knowledge-management Where an LLM System’s Knowledge Lives—and How to Change It Safely An LLM does not have a single knowledge store. A safe change starts with a precise contract, puts mutable information in the least-entangled suitable location, and verifies both the intended effect and everything that must remain unchanged. Aug 2026
08-30 finance Versioned LLM signals, separated trading authority A production reference architecture for turning an already-validated hybrid forecast into targets, independently risk-approved orders, venue events, and positions the firm can explain. 08-28 finance From Language to Incremental Edge: Engineering LLM Semantics into Quantitative Models An LLM becomes useful to quantitative finance only when its semantic measurements improve a strong point-in-time numerical forecast, remain calibrated out of sample, and survive realistic trading costs. 08-25 fine-tuning Can This Checkpoint Still Learn? Why retention and future learnability need separate tests in repeatedly trained neural networks. It frames a three-arm choice: continue, reset training state, or retrain from scratch.