Understand where your agent system will drift, decay, or break when the landscape changes — before it happens.
Systems that work in month one and fail in month twelve usually fail for the same reason: the landscape changed and the system didn't.
The frameworks were static while the domain evolved. The baselines were fixed while "normal" shifted. The intelligence layer accumulated data that aged silently — competitive signals from months ago sitting alongside yesterday's intelligence, both treated with equal confidence. The learning system applied the same feedback loop everywhere, regardless of whether the domain needed weekly observations or quarterly pattern detection.
The failure isn't dramatic. The agents don't break. They produce increasingly irrelevant output wrapped in high confidence. The symptoms are subtle: recommendations feel slightly off, analysis is technically defensible but doesn't match reality, the system just doesn't feel right anymore.
This package helps you find the fragility before it fails. Five skills covering dependency mapping, decay design, learning cycles, drift detection, and cascade tracing.
Trace which agents rely on which frameworks, data sources, upstream decisions, and shared intelligence. Build the graph that shows what's connected — so you know what a change will touch before you make it.
Assign the right decay strategy to each type of knowledge. Client intelligence decays on events, not calendars. Market signals decay at the speed of the market. Operational patterns decay when they stop being reinforced. Regulatory data is either current or it isn't. Blanket rules mishandle all of them.
Design domain-specific learning cycles instead of applying one generic feedback loop everywhere. Match the observation shape, pattern thresholds, tempos, and decay to the judgment each cycle is trying to improve.
Identify where scope definitions, baselines, frameworks, composition assumptions, and learning calibration could go stale when the landscape changes. The places where your system is vulnerable to silent drift.
Trace what breaks when a specific change happens. Framework updates, data source changes, upstream agent modifications, business rule shifts — each cascades through the system. Map the impact before it happens, including the silent failures that produce output without producing errors.
- Dependency Tracing — systematic approach to tracing dependencies across agents and frameworks
- Decay Strategies — the four decay profiles (event-driven, velocity-based, reinforcement-based, threshold-based)
- Data Freshness Architecture — building decay metadata into the data model
- Learning Cycle Anatomy — the five components of a domain-specific learning cycle
- Observation-to-Pattern Pipeline — structural separation between observation and action
- Drift Indicators — symptoms that indicate different types of drift
- Recalibration Patterns — designing systems that detect and correct their own drift
- Cascade Tracing — tracing change impact systematically
- Silent Failure Patterns — how cascades manifest as degradation rather than breakage
Start with Dependency Mapping to understand what's connected. Use Decay Profiling and Learning Cycle Design to build the mechanisms that keep the system current. Use Drift Surface to find vulnerabilities. Use Cascade Identification when you're planning a change or diagnosing a problem.
Created by Violet Fleming. These tools are grounded in a design philosophy developed across production multi-agent systems — the same thinking behind Orion.
Part of a suite of open-source agent design tools:
- Agent System Design — design and decompose agent systems
- Agent Knowledge Curator — capture institutional knowledge
- Agent Drift Prevention — prevent silent degradation
- Agent Edge Cases — failure modes you haven't considered
- Agent System Review — architectural review of existing systems
Licensed under CC BY-NC 4.0.