Level 4
Multi-Agent Fraud Consensus Engine
Coordinates specialist sub-agents for cross-tier validation before issuing claim payout mutations.
The workbench models enterprise agent architectures across five capability tiers, from predictive models to autonomous governance. Adjusting the input variance slider scales latency by error rate over twenty, shifting pipeline delay directly against your SLA threshold. Toggling Level 5 real-time audit adds ten compliance points and dynamically adjusts regulatory risk metrics in the leadership telemetry dashboard.
How does this workbench compute pipeline latency, per-resolution cost, and human-in-the-loop intervention across different architectural maturity tiers?
This workbench models enterprise agent architectures across five capability tiers (L1 statistical ML to L5 autonomous governance). Each scenario defines a graph of component nodes with preset base latencies and base unit costs. The simulation script aggregates these values into operational telemetry using simplified linear heuristics: pipeline latency scales with input variance, token pricing adds marginal costs to reasoning and delegation nodes (L2 and L4), human intervention rates decrease as architecture maturity level increases, and governance scores reflect the presence of L5 guardrails.
The formulas implemented in this tool are illustrative architectural approximations rather than measured production benchmarks. In real-world enterprise deployments, multi-agent LLM round-trips, tool execution latencies, streaming response times, retry cascades, and token usage depend heavily on model context size, network topology, external API rate limits, and actual prompt lengths rather than constant scalar node fixtures.
Select 'Legacy Predictive Analytics Only (Level 1)' from the Architecture Scenario dropdown. The scenario switches to 3 nodes with base latencies of 50 ms, 40 ms, and 35 ms (totaling 125 ms). At the default 3.5% input error rate, total latency is computed as Math.round(125 * (1 + 3.5 / 20)) = 147 ms, well within the 800 ms SLA budget. Because this scenario lacks L2 reasoning and L4 delegation nodes, token pricing does not add model overhead, yielding a cost of $0.01 per resolution, while Human Intervention rises to 57.3% due to the low architectural tier.
The client script evaluates four primary metrics from active scenario nodes. Aggregated latency is computed by summing the baseLatency of all active nodes and scaling by (1 + errorRate / 20). Cost per resolution sums baseCost across nodes and adds (count of L2 and L4 nodes) multiplied by (tokenCost / 100).
The human intervention rate is calculated from the scenario's highest architectural level: hitlBase = 60 - (maxLevel * 10), adjusted by (errorRate * 1.5) + ((hitlConf - 80) * 0.4), and clamped between 2% and 95%. Governance compliance score starts at 70, adds 12 points per L5 node, adds 10 points when Level 5 audit is toggled on, and subtracts (errorRate * 1.2).
The five tiers represent conceptual stages of agentic automation: L1 Predictive Engines (statistical models like scoring and classification), L2 Context & Reasoning (LLM prompt parsers and evaluators), L3 Tool & API Execution (read/write database and webhook integrations), L4 Multi-Agent Delegation (swarm orchestration and specialist agent consensus), and L5 Autonomous Governance (real-time policy guardrails and kill switches).