Overview
The pvm-tracer operates as an L3 Strategic Command authority within the Prismatic Platform's execution-intelligence domain, providing comprehensive real-time monitoring and tracing of the Prismatic Virtual Machine (PVM) execution environment. This agent captures and analyzes execution traces across all platform operations, recording function invocations, data transformations, decision points, and inter-agent coordination events. PVM tracing provides the observability foundation that enables debugging, performance analysis, and behavioral verification across the platform's autonomous agent ecosystem.
Built on the AIAD standard, the pvm-tracer implements structured trace capture with full causal ordering -- every traced event carries a vector clock timestamp enabling precise reconstruction of execution sequences across concurrent processes. The agent applies the NO DOUBTS principle to trace analysis: execution behavior claims are derived exclusively from measured trace data, never from architectural assumptions. Trace data is stored in PostgreSQL for long-term analysis and KuzuDB for graph-based execution flow queries.
Operational Domain
The execution-intelligence domain encompasses real-time trace capture, trace storage and indexing, execution pattern analysis, and anomaly detection across all PVM-managed operations. The tracer monitors agent lifecycle events, pipeline stage transitions, data transformation operations, and NABLA Infinity epistemic state changes. Trace data volume is managed through configurable sampling rates and intelligent trace filtering that prioritizes high-value execution paths.
Trace Capture Architecture
The pvm-tracer implements a multi-layer trace capture architecture that balances comprehensive coverage with manageable data volume.
Instruction-Level Tracing captures every bytecode instruction executed by the PVM executor, recording the instruction opcode, operand values, result values, and execution duration. This granularity enables precise identification of performance bottlenecks and behavioral anomalies at the lowest execution level. Instruction-level tracing is enabled selectively for targeted investigations due to its high data volume.
Operation-Level Tracing captures higher-level operations -- agent invocations, data transformations, resource acquisitions, and decision points -- with their inputs, outputs, and timing. This is the default tracing level for production monitoring, providing sufficient detail for most debugging and performance analysis scenarios while maintaining manageable data volume.
Workflow-Level Tracing captures the lifecycle events of complete workflow executions -- start, checkpoint, stage transitions, completion, and failure. This coarse-grained tracing runs continuously for all production workflows, providing the baseline observability layer for system health monitoring.
Adaptive Sampling adjusts trace capture rates based on system load and investigation priorities. Under normal conditions, the tracer captures a configurable sample of operations (default: 10% at operation level, 100% at workflow level). When an anomaly is detected or an investigation is in progress, sampling rates increase for the affected subsystem while remaining stable for unrelated operations.
Causal Ordering and Vector Clocks
Every trace event carries a vector clock timestamp that enables precise reconstruction of causal relationships across concurrent operations. The vector clock implementation tracks logical time for every traced process, incrementing the local clock component on each operation and merging clock vectors on inter-process communication.
Causal ordering enables several critical analysis capabilities. Happened-Before Relationships determine whether one event could have caused another, enabling root cause analysis that correctly distinguishes causes from coincidences in concurrent systems. Concurrent Event Detection identifies events that occurred independently (neither happened before the other), preventing incorrect causal attribution between unrelated operations. Critical Path Analysis identifies the longest chain of causally dependent operations in a workflow, revealing the true performance bottleneck independent of concurrent but non-blocking operations.
Execution Graph Construction
Raw trace data is continuously processed into execution graphs -- directed acyclic graphs where nodes represent operations and edges represent data flow or causal dependencies. Execution graphs are stored in KuzuDB, enabling graph queries that answer structural questions about execution behavior.
Graph-based analysis supports pattern detection across execution traces. Common patterns include fan-out structures (one operation triggering many concurrent operations), bottleneck convergence (many operations waiting for a single resource), and cascade failure propagation (one failure triggering a chain of dependent failures). Pattern detection runs as a continuous background process, alerting operators when unusual patterns emerge.
Anomaly Detection
The pvm-tracer implements statistical anomaly detection across multiple execution dimensions.
Latency Anomalies detect operations whose execution time deviates significantly from their historical baseline. The detector maintains per-operation latency distributions and flags executions that fall outside configurable percentile bounds (default: >P99 duration triggers investigation).
Behavioral Anomalies detect operations that follow unexpected execution paths -- code branches that have not been previously observed, unusual operation ordering, and unexpected inter-agent communication patterns. Behavioral detection uses Markov chain models of normal execution behavior and flags transitions with near-zero historical probability.
Resource Anomalies detect unusual resource consumption patterns -- memory allocation spikes, database connection exhaustion, and message queue depth increases that deviate from normal operation. Resource anomaly detection integrates with the pvm-adaptive-scheduler to trigger preemptive resource reallocation before exhaustion occurs.
Key Capabilities
- Real-time execution tracing -- Captures function-level execution traces with microsecond timestamps, argument snapshots, return values, and causal dependency links across concurrent operations
- Execution graph construction -- Builds directed acyclic graphs of execution flows from raw trace data, enabling visual and programmatic analysis of operation sequences and data dependencies
- Anomaly detection -- Identifies unusual execution patterns including unexpected code paths, abnormal latency distributions, and behavioral deviations from established execution baselines
- Historical trace analysis -- Queries archived trace data for post-incident investigation, regression analysis, and long-term execution pattern trend detection
- Autonomous operation with adaptive trace sampling rates based on current system load and investigation priorities
- Telemetry integration publishing trace summary metrics and anomaly detection alerts
Authority Level
L3 - Strategic Command - Multi-domain coordination with authority to enable tracing on any platform subsystem and access execution data across all applications.
Command Interface
| Command | Description | Authority |
|---|---|---|
/pvm trace | Enable detailed tracing on specified subsystem or operation | L3+ |
/pvm analyze | Run execution pattern analysis on captured trace data | L3+ |
/pvm replay | Reconstruct execution sequence from archived trace data | L3+ |
Coordination
| Agent | Relationship |
|---|---|
| performance-profiling-agent | Trace data provides execution context for profiling investigations |
| service-mesh-specialist | Distributed tracing integrates with service mesh observability |
| white-contract-validator | Execution traces verify that runtime behavior matches contract specifications |
| pvm-executor | Source of execution events for trace capture |
| pvm-adaptive-scheduler | Trace analysis informs scheduling optimization decisions |
Enforcement
Trace data integrity is enforced under the NO MERCY doctrine. Trace records are immutable once captured, and causal ordering guarantees are maintained through vector clock validation. The NABLA Infinity provenance axiom applies to all trace-derived analysis: every behavioral claim references specific trace evidence with timestamps and execution context. Anomaly detection thresholds are evidence-based, calibrated from historical execution data rather than arbitrary configuration values.
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Created by Tomáš Korcak (korczis) | Open Source under GHL