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Implements a task registry pattern that maintains task metadata (priority, status, dependencies) and provides real-time updates to connected dashboards via event emission or polling mechanisms.","intents":["I need to queue multiple AI agent tasks and execute them in priority order","I want to track which tasks are pending, running, completed, or failed across multiple agents","I need to prevent duplicate task execution and handle task retries automatically"],"best_for":["teams building multi-agent systems with concurrent task execution","developers implementing agent orchestration platforms","organizations managing long-running AI workflows with task dependencies"],"limitations":["No built-in distributed task persistence — requires external state store for durability across restarts","Task dependency resolution is linear; no support for complex DAG-based workflows","In-memory queue means task data is lost if the process terminates unexpectedly"],"requires":["Node.js 14+ or compatible JavaScript runtime","Event emitter or message queue for task notifications","Persistent storage backend if durability is required"],"input_types":["task objects with metadata (name, priority, payload, dependencies)","agent identifiers","status update events"],"output_types":["task queue state snapshots","task execution events","status change notifications"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agent-tower__cap_1","uri":"capability://automation.workflow.agent.execution.lifecycle.tracking","name":"agent-execution-lifecycle-tracking","description":"Tracks the complete lifecycle of agent execution from initialization through completion, capturing state transitions (idle → running → paused → completed/failed) with timestamps and execution metadata. 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Implements an append-only log pattern where all events are recorded and can be queried to reconstruct the complete history of an agent's execution.","intents":["I need to prove what decisions an agent made and when for regulatory compliance","I want to reconstruct the exact sequence of events that led to a particular outcome","I need to track who authorized or modified agent configurations for accountability"],"best_for":["organizations in regulated industries (finance, healthcare, legal)","teams implementing AI governance and compliance frameworks","developers building audit-ready agent systems"],"limitations":["Audit trail storage grows continuously; requires archival strategy for long-term retention","Querying large audit trails can be slow without proper indexing","No built-in anonymization; sensitive data in audit logs requires careful handling"],"requires":["Node.js 14+ with event sourcing or audit logging library","Persistent database (PostgreSQL, MongoDB) for audit trail storage","Optional: immutable storage (blockchain, append-only file systems) for tamper-proofing"],"input_types":["agent actions and decisions","configuration changes","user interactions","system events"],"output_types":["audit log entries with timestamps","audit trail queries and reports","compliance attestations"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agent-tower__cap_2","uri":"capability://tool.use.integration.multi.agent.coordination.and.communication","name":"multi-agent-coordination-and-communication","description":"Enables multiple AI agents to coordinate work through a message-passing or event-based communication layer, allowing agents to signal completion, share results, and synchronize on shared resources. Implements a publish-subscribe pattern where agents can emit events that other agents subscribe to, with optional message queuing for asynchronous coordination.","intents":["I need multiple agents to work on different subtasks and combine their results","I want agents to wait for each other to complete before proceeding (synchronization)","I need to broadcast results from one agent to multiple downstream agents"],"best_for":["teams building multi-agent AI systems with task decomposition","developers implementing agent swarms or hierarchical agent architectures","organizations coordinating specialized agents (research, planning, execution)"],"limitations":["No built-in deadlock detection — circular agent dependencies can cause hangs","Message ordering is not guaranteed in async scenarios without explicit sequencing","Scales to ~10-50 concurrent agents before event loop contention becomes problematic"],"requires":["Node.js 14+ with event emitter or message queue library","Shared context or message broker for inter-agent communication","Optional: distributed message queue (Redis, RabbitMQ) for multi-process agents"],"input_types":["agent instances","event/message objects with type and payload","subscription filters or routing rules"],"output_types":["event streams between agents","aggregated results from multiple agents","synchronization signals"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agent-tower__cap_3","uri":"capability://automation.workflow.dashboard.driven.agent.control","name":"dashboard-driven-agent-control","description":"Provides a web-based dashboard UI that allows operators to pause, resume, cancel, or restart running agents without code changes. Implements a command-dispatch pattern where dashboard actions are translated into agent control signals, with real-time feedback on whether commands succeeded or failed.","intents":["I need to stop a runaway agent without restarting the entire system","I want to manually intervene in an agent's execution when it gets stuck","I need to restart a failed agent from the dashboard without SSH access"],"best_for":["non-technical operators managing AI agent systems","teams requiring emergency stop capabilities for autonomous systems","developers debugging agent behavior in production-like environments"],"limitations":["Command latency depends on dashboard polling interval — typically 500ms-2s delay","No built-in role-based access control; requires external auth layer for production","Pause/resume only works for agents with explicit checkpoint support"],"requires":["Web browser with WebSocket or polling support","Backend API exposing agent control endpoints","Optional: authentication/authorization middleware"],"input_types":["user interactions (button clicks, form submissions)","agent state queries","control commands (pause, resume, cancel, restart)"],"output_types":["command acknowledgments","agent state updates","error messages or status notifications"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agent-tower__cap_4","uri":"capability://data.processing.analysis.agent.performance.metrics.collection","name":"agent-performance-metrics-collection","description":"Collects and aggregates performance metrics from running agents including execution time, resource usage (CPU, memory), task throughput, and error rates. Implements a metrics collection layer that hooks into agent lifecycle events and exposes metrics via a standardized interface for dashboard visualization or external monitoring systems.","intents":["I need to see how long each agent takes to complete tasks on average","I want to identify which agents are consuming the most resources","I need to track error rates and failure patterns to improve agent reliability"],"best_for":["teams optimizing agent performance and cost","organizations monitoring SLAs for AI-driven systems","developers profiling agent behavior to identify bottlenecks"],"limitations":["Metric collection adds ~2-5% overhead to agent execution time","No built-in time-series database — metrics are in-memory and lost on restart","Granular metrics (per-step timing) require agent instrumentation; not automatic"],"requires":["Node.js 14+ with performance monitoring APIs","Optional: time-series database (InfluxDB, Prometheus) for historical analysis","Optional: metrics visualization library (Grafana, Chart.js)"],"input_types":["agent execution events","resource usage snapshots","task completion records"],"output_types":["aggregated metrics (mean, p95, p99 latencies)","time-series data points","performance dashboards"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agent-tower__cap_5","uri":"capability://data.processing.analysis.task.result.aggregation.and.storage","name":"task-result-aggregation-and-storage","description":"Collects and stores results from completed agent tasks, providing a queryable interface to retrieve results by task ID, agent ID, or time range. Implements a result cache pattern with optional persistence to external storage, allowing downstream systems to access agent outputs without re-running tasks.","intents":["I need to retrieve the output of a completed agent task","I want to aggregate results from multiple agents into a single report","I need to store agent outputs for compliance or audit purposes"],"best_for":["teams building agent pipelines where downstream systems consume agent outputs","organizations requiring audit trails of agent decisions","developers debugging agent behavior by examining task results"],"limitations":["In-memory result storage is limited by available RAM; large results can cause memory pressure","No built-in deduplication — identical results from retried tasks are stored separately","Query performance degrades with large result sets; requires indexing for production use"],"requires":["Node.js 14+ with object storage or database","Optional: persistent database (PostgreSQL, MongoDB) for result durability","Optional: object storage (S3, GCS) for large result payloads"],"input_types":["task completion events with result payloads","query filters (task ID, agent ID, time range)","result metadata (status, timestamp, agent version)"],"output_types":["individual task results","aggregated result sets","result statistics"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agent-tower__cap_6","uri":"capability://automation.workflow.agent.error.handling.and.recovery","name":"agent-error-handling-and-recovery","description":"Implements automatic error detection, logging, and recovery strategies for failed agent tasks, including retry logic with exponential backoff, dead-letter queue handling, and error categorization. Uses a circuit-breaker pattern to prevent cascading failures when agents repeatedly fail on the same task type.","intents":["I need failed tasks to automatically retry with increasing delays","I want to categorize errors (transient vs permanent) and handle them differently","I need to prevent a single failing agent from blocking the entire system"],"best_for":["teams building resilient multi-agent systems","organizations running agents in unreliable environments (network-dependent)","developers implementing production-grade agent orchestration"],"limitations":["Retry logic assumes task idempotency; non-idempotent tasks may produce duplicate side effects","Circuit breaker thresholds require tuning per agent type; no automatic configuration","Dead-letter queue requires external storage; in-memory queue loses failed tasks on restart"],"requires":["Node.js 14+ with async/await support","Optional: persistent queue (Redis, RabbitMQ) for dead-letter queue","Optional: error tracking service (Sentry, Rollbar) for error categorization"],"input_types":["task execution errors with stack traces","error categorization rules","retry policy configuration"],"output_types":["retry attempts with backoff timing","dead-letter queue entries","error alerts and notifications"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agent-tower__cap_7","uri":"capability://automation.workflow.agent.configuration.and.deployment","name":"agent-configuration-and-deployment","description":"Manages agent configurations (model selection, parameters, system prompts) and enables deployment of new agent versions without system restarts. Implements a configuration registry pattern where agents load settings from a central store, with support for A/B testing different configurations across agent instances.","intents":["I need to change an agent's system prompt or parameters without restarting","I want to test a new agent configuration on a subset of tasks before rolling out","I need to version agent configurations and rollback to previous versions"],"best_for":["teams iterating on agent behavior and tuning parameters","organizations running A/B tests on agent configurations","developers managing multiple agent versions in production"],"limitations":["Configuration changes don't affect already-running tasks; only new tasks use updated config","No built-in conflict resolution for concurrent configuration updates","A/B testing requires manual traffic splitting; no automatic traffic allocation"],"requires":["Node.js 14+ with configuration management library","Configuration storage backend (file system, database, or config service)","Optional: version control system for configuration history"],"input_types":["agent configuration objects (model, parameters, prompts)","deployment targets (agent IDs or tags)","rollout strategies (immediate, gradual, canary)"],"output_types":["configuration deployment confirmations","active configuration snapshots","configuration version history"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agent-tower__cap_8","uri":"capability://automation.workflow.agent.resource.allocation.and.scaling","name":"agent-resource-allocation-and-scaling","description":"Monitors system resource usage and automatically scales the number of concurrent agents based on queue depth and resource availability. Implements a scaling policy engine that can apply different strategies (fixed pool, dynamic scaling, priority-based allocation) and enforces resource limits per agent.","intents":["I need to automatically spawn more agents when the task queue grows","I want to prevent resource exhaustion by limiting concurrent agents","I need to prioritize high-value tasks by allocating more resources to them"],"best_for":["teams running agents in cloud environments with variable load","organizations optimizing cost by scaling agents based on demand","developers implementing auto-scaling for agent systems"],"limitations":["Scaling decisions have ~1-2 second latency due to metric collection and policy evaluation","No built-in support for distributed scaling across multiple machines; assumes single-process pool","Resource limits are soft; agents can exceed limits if they consume resources unexpectedly"],"requires":["Node.js 14+ with process management or worker pool library","System resource monitoring (OS-level CPU/memory APIs)","Optional: container orchestration (Docker, Kubernetes) for distributed scaling"],"input_types":["queue depth metrics","system resource availability","scaling policy configuration","resource limit specifications"],"output_types":["scaling decisions (spawn/terminate agents)","resource allocation updates","scaling event logs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-agent-tower__cap_9","uri":"capability://automation.workflow.agent.logging.and.debugging","name":"agent-logging-and-debugging","description":"Captures detailed logs from agent execution including input prompts, model outputs, intermediate reasoning steps, and tool calls, with structured logging that enables filtering and searching. Implements a log aggregation layer that collects logs from all agents and exposes them via the dashboard with real-time streaming and historical search.","intents":["I need to see what prompts were sent to the model and what outputs were generated","I want to debug why an agent made a particular decision by examining its reasoning","I need to search logs across all agents to find patterns in failures"],"best_for":["developers debugging agent behavior and model interactions","teams troubleshooting production agent issues","organizations analyzing agent decision-making for transparency"],"limitations":["Detailed logging adds 10-20% overhead to agent execution time","Log storage can grow rapidly; requires log rotation or external log aggregation","Sensitive data (API keys, user PII) may be logged; requires careful filtering"],"requires":["Node.js 14+ with logging library (Winston, Pino, Bunyan)","Optional: log aggregation service (ELK, Splunk, CloudWatch) for production","Optional: structured logging format (JSON) for searchability"],"input_types":["agent execution events","model prompts and responses","tool call inputs and outputs","error stack traces"],"output_types":["structured log entries","log streams for real-time monitoring","log search results"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ or compatible JavaScript runtime","Event emitter or message queue for task notifications","Persistent storage backend if durability is required","JavaScript/TypeScript runtime with event emitter support","Optional: persistent database for historical lifecycle data","Optional: time-series database for execution metrics","Node.js 14+ with event sourcing or audit logging library","Persistent database (PostgreSQL, MongoDB) for audit trail storage","Optional: immutable storage (blockchain, append-only file systems) for tamper-proofing","Node.js 14+ with event emitter or message queue library"],"failure_modes":["No built-in distributed task persistence — requires external state store for durability across restarts","Task dependency resolution is linear; no support for complex DAG-based workflows","In-memory queue means task data is lost if the process terminates unexpectedly","Lifecycle events are ephemeral unless explicitly persisted to a database","No built-in support for distributed agents — assumes single-process or tightly coupled deployment","State machine validation adds ~5-10ms overhead per state transition","Audit trail storage grows continuously; requires archival strategy for long-term retention","Querying large audit trails can be slow without proper indexing","No built-in anonymization; sensitive data in audit logs requires careful handling","No built-in deadlock detection — circular agent dependencies can cause hangs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.15908418347816025,"quality":0.32,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.328Z","last_scraped_at":"2026-04-22T08:08:13.651Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":1949,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=agent-tower","compare_url":"https://unfragile.ai/compare?artifact=agent-tower"}},"signature":"4mi54auJquXpi0OYufSYlFkiLT5eTTH0IMZ5IPdbct05RiIYj43OAULtw+eYzao7ugGN4XqvhJbxICrXuoNOCA==","signedAt":"2026-06-21T07:11:34.783Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agent-tower","artifact":"https://unfragile.ai/agent-tower","verify":"https://unfragile.ai/api/v1/verify?slug=agent-tower","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}