Agentic artificial intelligence, in which a model reasons, calls tools, and acts in a closed loop rather than emitting a single prediction, is arriving in medicine, where the tolerance for confident error is close to zero. This paper takes a design stance on healthcare agentic AI: rather than propose a new model, it asks how to build a clinical agentic system so that its autonomy is bounded by verification and human oversight. We distil six design requirements from the clinical AI literature, covering harm minimisation, human oversight, calibrated uncertainty, verification and provenance, equity, and interpretability. We give a reference architecture in which a reasoning agent is wrapped by a verification gate and a clinician escalation path, and we formalise that gate. We prove two guarantees: escalation is monotone in safety, so widening it never increases expected harm, and a risk-weighted gate minimises expected harm for any fixed amount of clinician effort. We validate the design with a fully reproducible simulation of an agentic clinical triage loop. The escalation gate trades autonomy for safety along a smooth curve, halving expected harm as oversight rises; layering an independent verifier and human review cuts the unsafe-action rate from 0.230 with the agent alone to 0.077; and risk-weighted escalation attains lower harm than confidence-only escalation at every clinician budget, exactly as the theory predicts. The results argue that in medicine the safety of an agentic system is engineered at the level of the loop, through verification, risk-weighted escalation, and clinician oversight, and can be designed and reasoned about rather than left to chance.
Setu S. M. Kodi, Sudheer Singamsetty· International Journal of Com...· 0 citations
Large scale agentic artificial intelligence systems retrieve evidence from vector databases that store dense embeddings alongside structured, numeric, and temporal attributes, and they turn that evidence into business decisions through repeated reasoning loops. Managing this multi-model data at enterprise scale raises three coupled questions: how to index billions of vectors so that retrieval is both accurate and fast, how to store them compactly enough to fit memory budgets, and how to engineer the decision loop so that it converges rather than drifts. This paper gives a unified mathematical and algorithmic treatment of these questions and introduces a proposed architecture, the Multi-Model Vector Loop Engine, that couples a quantized vector index with a feedback controlled agentic decision loop. We formalize approximate nearest neighbour retrieval, product quantization, hybrid multi-model filtering, and the decision loop as a fixed point iteration, and we prove that under a contraction condition the loop converges geometrically. We implement the full stack from first principles and report reproducible measurements on one hundred thousand 128-dimensional embeddings. An inverted file index raises recall at ten from 0.546 at a single probe to 1.000 at thirty two probes, product quantization trades memory for accuracy from recall 0.258 at 128 times compression to 0.701 at 16 times compression, a hybrid query with 4.0 percent selectivity resolves in 0.043 milliseconds, and the agentic loop converges to its fixed point within three iterations while raising cumulative coverage and modelled decision utility. A single ingestion shard encodes about 48800 records per second, so linear sharding reaches the five million records per second target with 103 shards. The results give practitioners a rigorous basis for managing vector and multi-model data under the accuracy, memory, and decision quality constraints of agentic enterprise systems.
Sudheer Singamsetty· International Journal of Com...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
