How to Make AI Agents Reliable Before They Act
A practical guide to reliable AI agents, deterministic fallbacks, refusal boundaries, evidence gates, and proof before autonomous action.
Problem-Oriented Entry Points
Guides
Problem-oriented guides for reliable AI agents, grounding, fallback, and bounded self-improvement.
AI Hallucination Is Not the Only Problem
A grounded explanation of AI hallucination, self-certification failure, external evidence, unresolved states, and why confident answers are not enough.
Bounded Self-Improving AI Agents
How to discuss self-improving AI agents without ignoring boundaries, drift, review state, and proof limits.
AI Agent Hallucination Risk Before Tool Use
RAG Grounding vs AI Self-Certification
Research paths
A question, a method, something you can inspect.
Can an answer change the situation it describes?
ReflexBench compares observer-participant reasoning across four observer-depth levels. The public browser contains 20 scenarios; its orientation receipt is not an automated benchmark score.
Does feedback reduce repeated mistakes?
WisdomBench examines failure and feedback over repeated episodes. Check the released task protocol, scoring and run evidence before interpreting improvement as general learning.
What must be checked before an agent acts?
Proof-Carrying Action separates evidence and permission from fluent answers. The mini gate illustrates ACT, WAIT, QUERY and REFUSE; passing it does not certify a deployed system.
Read the linked artifact version and claim scope together. Public archives and our own repositories provide inspectable material, not independent validation of commercial products.