{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp09_archetype_breadth150_20260804","cell_id":"emergent_pattern_detection__computer_science","arm":"BREADTH_PROBE_ONE_SHOT","candidate_id":"emergent_pattern_detection__computer_science__P1","proposal_index":1,"version":0,"title":"Retry-Coupling Sentinel for Emerging Microservice Retry Storms","problem":"In a microservice system, many clients and services independently retry failed or slow requests. Each retry may comply with its local policy, yet overlapping timeouts, fan-out, and backoff schedules can make retries reinforce congestion across service boundaries. No component sees enough of the interaction graph to recognize the forming retry storm before ordinary availability alarms fire.","actors":["Service-owning engineering teams","Site reliability engineers","Incident commander","Platform observability team","Users whose requests traverse the affected services"],"observable_state":"Distributed traces and service telemetry show a growing share of requests descended from earlier failed or timed-out attempts; retry descendants increasingly cross multiple service edges, cluster within overlapping backoff windows, and coincide with rising queue time while original user-request volume remains comparatively stable. Each service's retry count may still fall within its local limit.","consequence":"Retry-generated work consumes shared compute, connection, and queue capacity, prolongs latency, causes additional timeouts, and induces further retries. The resulting positive feedback can turn a recoverable local slowdown into a system-wide availability incident.","affected_objective":"Preserve end-to-end service availability and latency by identifying cross-service retry amplification while there is still a legitimate opportunity to investigate, redirect, or contain it.","intervention":"Add a shadow-mode Retry-Coupling Sentinel to existing trace processing. Mark retry attempts and their originating request lineage without retaining payloads or user identifiers; aggregate lineages by service edge, endpoint class, failure category, deployment context, and short time window; compare retry-descendant growth, fan-out, queue delay, and synchronization against matched historical variation; and emit an uncertainty-labeled pattern hypothesis when multiple local retry loops appear to be mutually amplifying. A human review panel classifies each hypothesis as expected recovery, ambiguous coupling, or harmful emergence. Expected recovery is recorded, ambiguous coupling opens a time-bounded investigation, and harmful coupling may be routed to the incident commander for an independently authorized canary using an existing per-route retry cap, backoff adjustment, or circuit breaker. The sentinel then compares post-response traces with the hypothesis and revises its aggregation and thresholds.","structural_mapping":[{"archetype_element":"Local Signal Collection","domain_realization":"Retry markers, request-lineage links, timeout and failure categories, service edges, queue delays, endpoint classes, and deployment context from distributed traces and service telemetry."},{"archetype_element":"Aggregation Rule","domain_realization":"Group retry descendants across services and short time windows by originating lineage and call-graph edge, preserving endpoint and failure context while excluding payloads and user identity."},{"archetype_element":"Baseline and Variation Frame","domain_realization":"Compare each candidate with matched periods for traffic level, endpoint class, deployment state, and known dependency incidents so routine recovery bursts are not treated as emergence."},{"archetype_element":"Pattern Detector","domain_realization":"Surface the joint signature of expanding retry ancestry, cross-edge fan-out, synchronized attempts, and worsening queues without a corresponding rise in original request volume."},{"archetype_element":"Context Marker","domain_realization":"Attach affected service edges, failure categories, recent deployments, trace-sampling coverage, and the local retry policies contributing evidence to each hypothesis."},{"archetype_element":"Desirability Classification","domain_realization":"Human reviewers label the hypothesis expected recovery, ambiguous coupling, or harmful emergence and record confidence, affected services, and evidence gaps."},{"archetype_element":"Response Rule","domain_realization":"Record expected recovery, investigate ambiguous coupling, or route suspected harmful coupling to an incident commander who may separately authorize a bounded retry-control canary."},{"archetype_element":"Feedback Review Loop","domain_realization":"Check whether the predicted retry lineage and congestion pattern changes after investigation or an authorized canary, then update signal definitions, matching variables, and thresholds."},{"archetype_element":"Privacy and Legitimacy Guardrail","domain_realization":"Process service-level operational metadata only, prohibit payload and user-identity collection, restrict access to reliability roles, and forbid using sentinel output for individual employee evaluation."}],"mechanism_mapping":[{"mechanism_slug":"weak_signal_aggregation","role":"Combines individually ordinary retry attempts across request lineages, service edges, and time windows so early reciprocal amplification becomes inspectable.","counterfactual_removal":"Without cross-service aggregation, each team sees only locally acceptable retries and the macro-pattern remains fragmented."},{"mechanism_slug":"trend_detection","role":"Tracks whether retry-descendant fan-out and queue delay strengthen over successive windows rather than appearing as a brief recovery burst.","counterfactual_removal":"Without directional evidence, isolated spikes are more likely to be confused with a forming feedback loop."},{"mechanism_slug":"anomaly_detection","role":"Compares lineage-level retry coupling with matched baseline variation to nominate unusual interaction patterns for review.","counterfactual_removal":"Without a variation frame, ordinary retry behavior would generate unstructured alerts and weaken interpretability."},{"mechanism_slug":"emergent_behavior_dashboard","role":"Shows the interaction graph, supporting traces, uncertainty, classification, ownership, and response status on one review surface.","counterfactual_removal":"Without a shared review surface, the hypothesis may remain split among service dashboards and lose its explicit response owner."}],"causal_chain":["A dependency slowdown causes scattered timeouts across several callers.","Local clients and services independently initiate retries according to their own policies.","Retry attempts add load to shared queues and dependencies, extending latency for original and retried requests.","Extended latency produces more timeouts and additional retry descendants across the call graph.","The sentinel links local attempts to request lineages and aggregates their timing and cross-service propagation.","Matched-baseline detection surfaces a candidate mutually reinforcing retry pattern with visible uncertainty and context.","Human classification distinguishes expected recovery from ambiguous or harmful emergent coupling.","A classified hypothesis is recorded, investigated, or routed to an incident commander for a separately authorized bounded response.","Post-response review tests whether the anticipated interaction pattern changed and updates the detector."],"baseline":"Existing service dashboards alert on aggregate error rate, latency, saturation, and per-service retry counts. They can reveal degraded components, but they do not reconstruct retry ancestry across service boundaries, distinguish original demand from retry-generated demand, or classify a forming interaction pattern with an owned response pathway.","nearest_rivals":["Per-service retry budgets or static maximum-attempt policies, which constrain local behavior but do not detect mutually reinforcing behavior across independently configured services.","Application-performance monitoring and distributed tracing, which expose requests and dependencies but ordinarily require an operator to infer the emerging macro-pattern.","Circuit breakers and load shedding, which can contain overload after a trigger but do not determine whether distributed retries are collectively creating it.","Single-incident root-cause analysis, which attributes an observed incident after the fact rather than aggregating current local interactions into an early pattern hypothesis."],"remaining_contrastive_claim":"The candidate's distinctive claim is limited to operational structure: reconstructing retry ancestry across multiple service edges, comparing its joint growth with contextual baselines, preserving uncertainty, and coupling the resulting macro-pattern hypothesis to a legitimate human-owned response rule could reveal distributed retry amplification that isolated retry counters and generic saturation alerts do not explicitly represent.","authority_safety":{"decision_authority":"The platform observability owner may operate the shadow detector and convene review; affected service owners validate local context; only the designated incident commander or change authority may approve any production retry-cap, backoff, circuit-breaker, or load-shedding change.","authorized_first_step":"Run an offline replay and then a shadow-only observation on a small, preselected set of services using existing trace metadata; emit hypotheses to reviewers without changing production traffic or configuration.","excluded_actions":["Automatic modification of retry, timeout, routing, circuit-breaker, or load-shedding settings","Collection or retention of request payloads, credentials, or user identifiers","Employee performance scoring or punitive attribution based on contributing service configurations","Broad deployment beyond the selected services before review of sampling quality and false hypotheses","Treating a detector alert as proof of root cause"],"halt_rollback":"Disable sentinel outputs and remove its derived lineage aggregates if trace-processing overhead exceeds the agreed budget, access controls fail, prohibited data enters the pipeline, or reviewers find repeated misleading hypotheses. Any later response canary must have its own stop condition and restore the prior configuration through the existing change-management path."},"negative_tests":{"strongest_counterevidence":"The apparent cross-service growth disappears after correcting for trace sampling, duplicate spans, batch traffic, or a known workload surge, or the same signature regularly occurs during healthy recovery without leading to congestion.","problem_falsifier":"Across reviewed degradation episodes, overload is adequately explained by a single component or centrally initiated traffic change, and retry-lineage interactions neither precede nor materially clarify the system-level transition.","intervention_falsifier":"In bounded replay and shadow observation, the sentinel fails to surface reviewer-confirmed cross-service retry coupling before existing operational alerts, or its nominated patterns cannot be distinguished from matched healthy recovery periods.","risks":["False positives could prompt unnecessary investigation or premature containment.","Trace sampling gaps or inconsistent retry markers could manufacture or hide apparent lineages.","Additional trace processing could burden the observability pipeline during incidents.","Aggregation could erase service-specific context and unfairly implicate a team or component.","Operators could over-trust a visually persuasive interaction graph despite visible uncertainty.","Services may adapt their retry implementation in ways that make detector thresholds stale.","A response to one retry loop could shift congestion or failure to another dependency."]},"next_evidence_step":"Using only already-retained, policy-compliant telemetry, select a bounded corpus containing a small number of known degradation windows and matched healthy recovery windows from the same services. Have two reliability reviewers independently label whether cross-service retry amplification is supported without seeing sentinel output; run the proposed lineage aggregation in replay; then compare alert timing, hypothesis agreement, false hypotheses, missing context, sampling artifacts, and processing cost against existing alerts. Stop after this corpus and decide only whether a shadow pilot is warranted.","prior_art_status":"UNSEARCHED","diversity_from_prior_proposals":"Not assessed because runtime isolation prohibits inspection of other proposals or experiment candidates; this record stands only as the single candidate for the supplied cell.","revision_record":{"parent_version":null,"progress_targets_addressed":["One-shot breadth probe completed from the supplied archetype and domain card."],"conceptual_changes":[],"operational_changes":[],"evidence_changes":[],"claim_changes":[]}}