Agent Retention Architecture Evaluator

Trigger & Event Ingestion

LLM Decider & Compactor

Autonomous Tool Executor

Human Verification Gate

Rollback & Recovery Bus

💡 Click/drag nodes to reshape workflow topology. Click a node to inspect failure rates. Ready

Retention & Trust Health ● Dynamic Markov Engine

30-Day Retention Probability 84.2% Benchmark: >75% for daily retain

Trust Half-Life 42 days Time to silent user abandonment

Human Interruption Drag 14.2% Verification queue friction

Autonomous Drift Risk 12.0% Unchecked hallucination rate

Root Cause & Structural Friction

Primary Friction Bottleneck Verification latency during burst hours

Architectural Recommendation Decouple read evaluation from write-back queues

Abandonment Risk Factor False-positive alert fatigue leads users to disable proactive runs after ~6 weeks.

Architecture Parameters

Context Drift & Flakiness Rate 12%

Human-In-The-Loop Gate Frequency

Compaction & Memory Token Window 8k tokens (Strict)

Auto-Rollback & Retry Limit 2 Retries

Counterfactual Stress Injection

Agent Retention Architecture & Scoring Dynamics

How do human-in-the-loop gates, context drift, and retry buffers interact to determine the calculated retention score in this agent simulation?

This interactive workbench models multi-step agent topologies using Cytoscape.js and computes illustrative reliability metrics. Rather than running an empirical Markov chain transition matrix or telemetry-backed user retention study, the application evaluates an algebraic heuristic: it subtracts penalties for context drift and human interruption overhead, adds logarithmic bonuses for context window size and retry allowances, and adjusts for gate frequency. The resulting 30-Day Retention Probability and Trust Half-Life serve as exploratory architectural benchmarks rather than validated production forecasts.

The retention indices, trust half-lives, and failure percentages are synthetic pedagogical models hardcoded inside calculateRetentionModel(), not measurements derived from live production agents or real human user cohorts. In addition, the tool contains an explicit hardcoded fixture setting retention to 84.2% and half-life to 42 days whenever default parameters are active.

Try a worked example

Under the default Autonomous Inbox Triage preset with a 12% drift rate, 8k token window, and 2 retries, clicking 'Full Auto (No Gate)' sets the human gate drag to 1.0% and applies an autonomous penalty of -32.4 (-18.0 * 1.8). The model recalculates 92.0 - (0.12 * 90.0) - (1.0 * 0.25) + log2(8)*1.5 + 2*2.2 - 32.4 = 57.45%, which rounds to 57.5% for the 30-Day Retention Probability and yields a Trust Half-Life of 29 days (round(57.5 / 100 * 50)).

Interactive Topology Graph via Cytoscape.js

The canvas visualizes agent architectures using Cytoscape.js, an open-source graph theory and network visualization library supporting interactive node and edge rendering, breadth-first layout, and styling. Preset workflows—such as Inbox Triage and Local Code Refactoring—render directed acyclic pipelines connecting ingestion triggers, LLM deciders, mutation gates, tool executors, and rollback buses. Cytoscape.js Graph Theory & Network Library Documentation

When a node is tapped, Cytoscape highlights the element using border and shadow styles while displaying the active label in the toolbar. Clicking 'Run Simulation' triggers pulse animations along edge paths using temporary CSS classes ('active-pulse'), incrementing the simulated cycle counter by 25 runs per click. Cytoscape.js Graph Theory & Network Library Documentation

Mathematical Formulation of the Retention Heuristic

Despite labeling the telemetry as a 'Dynamic Markov Engine', the underlying script uses an algebraic heuristic: Retention = 92.0 - (Drift * 90.0) - (GateDrag * 0.25) + (log2(TokenBudget) * 1.5) + (Retries * 2.2) + GateTrustMod. Counterfactual stress modes (overflow, outage, queue, silent drift) apply discrete deductions ranging from -15 to -25 points.

Gate frequency alters both gateDrag and gateTrustMod: 'Always' imposes 38% drag and a -12 point penalty due to fatigue; 'Mutations Only' balances 14.2% drag with a +4 bonus; 'Full Auto' reduces gate drag to 1.0% but incurs an autonomous error penalty of up to -32.4 points when drift exceeds 8%.

Sources and further reading

Cytoscape.js Graph Theory & Network Library Documentation
Verification of Cytoscape.js graph rendering, breadthfirst layouts, node/edge styles, event handlers, and animation methods.

A heuristic retention index and its default override

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Read the explanation

The local retention heuristic combines a ninety two baseline, drift penalty, approval drag, logarithmic token benefit, retry benefit and gate trust adjustment. With default drift point twelve, token budget eight, retry limit two and mutation-only approval, arithmetic gives ninety point five five. The exact inbox default then replaces it with eighty four point two. This is an authored index, not a measured retention probability or observed user behavior. Activating overflow prevents the default override and subtracts twenty two from the general formula, producing sixty eight point five five before display rounding. It does not subtract twenty two from eighty four point two. The visible rounded result is sixty eight point six. Other stress labels subtract eighteen, fifteen or twenty five. These penalties simulate assumptions, rather than detected outages, actual queue time or silent failures. The displayed trust half life is the rounded index divided by one hundred times fifty. Default eighty four point two produces forty two days; overflow sixty eight point five five produces thirty four. There is no fitted survival model or user cohort. Narrative friction and abandonment statements come from configured branches. Model and architecture labels do not invoke agents or databases. No backend or provider API was used to author or check this explanation.