A topology map shows what is connected to what. The question in front of it is usually another: may this run here, and is it ready?

A control-plane view for agent systems. The same nodes rearrange by governance, readiness, provenance or risk; an operator chat answers by reading the live cluster; and changes pass a gate that refuses by default and keeps its reason. Two recorded demos: gene agents with a quantum capability, and an ops agent on live Kubernetes.

Agent Scope draws an agent system — the agents, their policies, gateways, data sources and the workloads they act on — as one set of nodes and edges, and lets the viewer choose what anchors the layout. Ten projections ship: eight built in, and two that a Kubernetes profile contributes at runtime, which the client has never heard of and does not need to. Each link carries a small envelope of values the control plane already knows, such as policy compatibility, trust, readiness and provenance, and every value also drives its own visual channel, so one link can read as well governed and poorly provenanced at the same time.

Questions asked in words are answered by issuing real read commands against the live cluster — pods, events, logs, nodes, namespaces — and reporting what came back, not by inferring from the prompt. Asked which pods in a namespace were not ready, it named six, and kubectl showed the same six.

Mutations pass an admission gate first. In the recorded run, scaling a standard-tier workload from 2 to 4 replicas was admitted and reached the cluster; the same request against a critical-tier workload was refused, and its replica count stayed at 3. The only difference was a label on the resource, so the decision is made on data rather than in code. A mutation needs a proof-of-admission token that only the gate can construct; a request no rule matches is refused; and allows and denials alike leave a decision artifact with the rule, the reason and a trace id.

The first demo puts three agents on real clinical-evidence records from CIViC for EGFR, TP53 and BRCA1. Each agent holds a versioned policy file and cites its id, version and sha256 on every decision; at the default evidence floor, 140 of 269 EGFR items are refused for therapy claims, because the finding stands but using it to say a treatment will work does not. TP53 may request one capability, a QAOA contig assembly following the published QuASeR method on a simulator, and only through a gateway with its own allow-list. The run returns the correct ordering, reported with its margin of 0.0005 over the runner-up, and a request for real quantum hardware is refused by the gateway before anything is submitted.

The second demo gives an operations agent seven orders in chat on a live k3s cluster that it reaches only through a scoped service account. A read is answered from the cluster; an ordinary scale and an ordinary create are allowed; scaling a critical workload is escalated and changes nothing; a create that labels itself critical is refused as a claim by the order rather than a fact; a manifest from a public repository is decided object by object, one applied and two refused; and asked whether the cluster matches its repository, it answers true only when both sides answer. Every order ends with the cluster read back, and every record goes to a hash-chained ledger that catches an edited or dropped line on restart.

Everything described here runs and is measured on a real cluster: the projections, the read commands, the enforced gate and the decision artifacts, kept on a hash-chained ledger that survives a restart. The site publishes that status row by row, and the next step, reading policy from a cluster resource, goes through the one seam where policy is already assembled. The trace view in both demos is the published @agent-scope-ca/graph-canvas component, available on npm.

References

1. Sarkar, Al-Ars and Bertels (2021). QuASeR: Quantum Accelerated de novo DNA sequence reconstruction. PLOS ONE 16(4), e0249850. doi:10.1371/journal.pone.0249850
2. Griffith et al. (2017). CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nature Genetics 49(2), 170–174. doi:10.1038/ng.3774
3. Bergholm et al. (2018). PennyLane: Automatic differentiation of hybrid quantum-classical computations. arXiv preprint. arXiv:1811.04968

agentscope.cagenequbit.agentscope.cainfra.agentscope.camassivegpu.casemanticgateway.caGene-agents video: Zenodo 10.5281/zenodo.23006165Ops-agent video: Zenodo 10.5281/zenodo.23006181@agent-scope-ca/graph-canvasok-admission-gate-demo