Overview

/ecosystem is a production command in the Framework category of the Prismatic Platform that provides a comprehensive real-time overview of the entire platform ecosystem, including all umbrella applications, agents, commands, quality metrics, and infrastructure status. The platform consists of nearly 100 OTP applications, over 400 AIAD agents, and 228 slash commands, making a unified visibility tool essential for understanding the health and state of the system at any point in time.

This command operates under the L2+ authority level and is executed by the ecosystem-coordinator agent. It is part of the platform's 216-command slash command registry, built on the AIAD (Autonomous Intelligence Agent Design) standard. The ecosystem coordinator agent aggregates status information from multiple sources, including OTP application supervisors, the AIAD registry, the quality gate system, and the telemetry infrastructure.

Understanding the ecosystem as a whole is critical for making informed decisions about where to invest development effort, which components are healthy, and where bottlenecks or degradation might be occurring. The /ecosystem command replaces the need to manually query multiple subsystems and synthesizes their outputs into a single coherent view.

Architecture

The ecosystem monitoring system is structured as a multi-source aggregation pipeline that collects status data from every major platform subsystem and presents it through a unified interface.

Data Collection Architecture

Source	Method	Data Provided
OTP Applications	`Application.started_applications/0`	App list, versions, dependency tree
AIAD Registry	Registry ETS scan	Agent count, command count, status
Quality Gates	`mix quality.gates --json`	Score per domain, overall composite
Telemetry	`:telemetry` event aggregation	Request rates, error rates, latencies
Git Repository	`git ls-tree`	File counts, recent changes, LOC
Supervision Trees	`Supervisor.which_children/1`	Process health, restart counts
ETS Tables	`:ets.info/1`	Memory usage, table sizes

Aggregation Pipeline

OTP Apps ----+
AIAD Registry --+
Quality Gates ---+--> Aggregator --> Scorer --> Formatter --> Output
Telemetry ------+         |
Git Repo -------+         v
Supervision ----+    Health Engine
ETS Tables -----+    (composite score)

Health Scoring Model

The ecosystem health score is a weighted composite of individual subsystem health indicators.

Domain	Weight	Healthy Threshold	Critical Threshold
Compilation	20%	0 warnings	Any warning
Test Suite	20%	100% pass	< 95% pass
Quality Score	20%	95+ / 100	< 80 / 100
Agent Availability	15%	> 98% agents responsive	< 90% agents responsive
Process Health	15%	0 abnormal restarts	> 5 restarts in 10 min
Memory Usage	10%	< 80% of allocation	> 95% of allocation

Usage

# Full ecosystem overview
/ecosystem

# Show only application status
/ecosystem --apps

# Show only agent status
/ecosystem --agents

# Show only quality metrics
/ecosystem --quality

# Show infrastructure health
/ecosystem --infrastructure

# Generate machine-readable output
/ecosystem --format=json

# Show detailed view of a specific subsystem
/ecosystem --detail=prismatic_web

# Compare ecosystem state over time
/ecosystem --history --since="2026-01-01"

# Show dependency graph
/ecosystem --deps

# Quick health check only
/ecosystem --health

Options

Option	Type	Default	Description
`--apps`	flag	false	Show only umbrella application status
`--agents`	flag	false	Show only AIAD agent status
`--quality`	flag	false	Show only quality gate metrics
`--infrastructure`	flag	false	Show infrastructure health metrics
`--detail`	string	none	Detailed view of a specific subsystem
`--deps`	flag	false	Display dependency graph
`--health`	flag	false	Quick composite health check
`--history`	flag	false	Show ecosystem metrics over time
`--since`	date	30 days ago	Start date for history view
`--format`	string	text	Output format: text, json, markdown, table
`--verbose`	flag	false	Include per-component details
`--sort`	string	name	Sort order: name, health, size, activity
`--filter`	string	none	Filter components by pattern

Execution Flow

Source Discovery: The ecosystem coordinator identifies all available data sources by scanning OTP applications, the AIAD registry, and the infrastructure configuration.
Parallel Collection: Status data is collected from all sources in parallel using Task.async_stream for optimal performance. Each source has a configurable timeout (default 5 seconds) to prevent slow sources from blocking the overall report.
Data Normalization: Raw data from heterogeneous sources is normalized into a common schema with standardized health indicators (healthy, degraded, critical, unknown).
Health Scoring: The composite health score is calculated from individual subsystem scores using the weighted model described in the Architecture section.
Trend Analysis: If historical data is available, trends are computed for key metrics including quality score, test pass rate, agent availability, and compilation status.
Report Generation: The normalized data, health scores, and trend analysis are formatted into the requested output format.
Telemetry Emission: The ecosystem status itself is recorded as a telemetry event for historical tracking.

Integration Points

Component	Relationship	Details
Prismatic Agents	Executed by `ecosystem-coordinator`	Aggregates status from all agents
AIAD Registry	Agent and command inventory	Source of agent/command counts and status
Quality Gates	Quality metrics source	13 quality domains with scores
Telemetry	Metrics aggregation	Runtime performance and health metrics
SEADF	Evolution status	Self-evolution subsystem health
Session Lifecycle	Session tracking	Active session count and health
Git Trees	Codebase metrics	File counts, LOC, recent changes
OTP Supervision	Process health	Supervisor tree status and restart counts

Best Practices

Start sessions with ecosystem check. Running /ecosystem --health at the beginning of a development session provides immediate awareness of the platform state. This is especially valuable after pulling changes from other contributors.

Monitor trends, not just snapshots. The --history option reveals whether the ecosystem is improving or degrading over time. A slowly declining quality score that is still above threshold is more concerning than a momentary dip that recovers quickly.

Use detail mode for investigation. When the overview shows a degraded subsystem, use --detail=subsystem_name to drill into the specific component and identify the root cause.

Integrate with CI/CD. Use /ecosystem --format=json --health as a CI pipeline stage to automatically block deployments when the ecosystem health drops below acceptable levels.

Track agent availability. With over 400 agents, availability monitoring catches configuration issues, missing dependencies, and process crashes that might otherwise go unnoticed until a dependent command fails.

Error Handling

Error	Cause	Resolution
`source_timeout`	A data source did not respond within timeout	Source is marked as "unknown" in report; investigate the specific subsystem
`no_applications_running`	OTP applications not started	Ensure the Prismatic application is running (`iex -S mix`)
`aiad_registry_unavailable`	AIAD registry ETS table not initialized	Run `./.aiad/bin/aiad index` to rebuild the registry
`quality_gates_failed`	Quality gate check command returned error	Run `mix quality.gates` directly to see detailed errors
`insufficient_history`	Not enough historical data for trend analysis	Accumulate more data points or reduce `--since` window
`invalid_subsystem`	`--detail` references non-existent subsystem	Use `--apps` to list valid subsystem names

Advanced Usage

Ecosystem Comparison Across Branches

Compare ecosystem health between the current branch and main to assess the impact of in-progress work.

# Snapshot current state
/ecosystem --format=json --output=branch-state.json

# Switch to main and compare
git stash && git checkout main
/ecosystem --format=json --output=main-state.json
git checkout - && git stash pop

# Compare the two snapshots
/ecosystem --compare=main-state.json,branch-state.json

Custom Health Thresholds

Override default health thresholds for specific environments or quality standards.

# Stricter thresholds for production readiness
/ecosystem --health --threshold="quality:98,tests:100,warnings:0"

# Relaxed thresholds for development branches
/ecosystem --health --threshold="quality:80,tests:90"

Automated Ecosystem Reports

Generate periodic ecosystem reports for stakeholder communication.

# Weekly ecosystem report
/ecosystem --format=markdown --history --since="7 days ago" --output=weekly-report.md

# Monthly comprehensive report
/ecosystem --format=html --verbose --history --since="30 days ago" --output=monthly-report.html

SEADF Integration

Feed ecosystem data directly into the SEADF evolution engine for autonomous improvement targeting.

/ecosystem --format=json | mix seadf.ingest --source=ecosystem-status

Doctrine Compliance

All commands operate under the NO MERCY, NO DOUBTS doctrine:

NO MERCY: Zero tolerance for incomplete execution or quality violations. Ecosystem monitoring exposes every quality gap without hiding uncomfortable truths.
NO DOUBTS: Full investigation before action, evidence-based results. All health scores are computed from verifiable data sources with full provenance.

/seadf - Self-Evolving Autonomous Development Framework control and monitoring
/agents - List and manage agent ecosystem with status monitoring
/quality-gates - Enforce quality gate checkpoints with zero-warning compilation validation
/analyze - System architecture analysis with dependency mapping
/git-trees - Git tree-based codebase exploration at ~100x speed improvement
/inject - AIAD injection coordination for pattern and agent deployment
/integrate - Cross-system integration design and implementation

Connect & Contribute

Created by Tomáš Korcak (korczis) | Open Source under GHL

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/ecosystem