A fully autonomous SDLC with 22 quality analyzers, 15 security scanners, adversarial review, mutation testing, and cryptographic attestation on every release.
Nineteen integrated domains, from requirements through attestation — each one a working system, documented and scanned.
Tier classification picks the workflow, the kernel dispatches the agents, and every handoff is gated before the next one starts.
37 specialized agents route every task through the right expertise. 5-signal classification determines complexity, and tiered quality gates ensure nothing moves forward without review.
Extensible at every level. 9 lifecycle hooks intercept every tool call, every session start, every context compression. Skills, commands, and MCP integration let you customize the entire pipeline.
Automated failure recovery with pattern-based classification, YAML strategy playbook, checkpoint-aware restart, and model tier downgrade. Fail open — recovery never masks real errors.
Centralized event taxonomy with YAML routing rules, n8n workflow registry, dead letter queue with replay, and SLA-tracked workflow health monitoring across all 15 automation workflows.
Tiered vector memory, a governed process-knowledge base, and context-window monitoring that escalates before it truncates.
Agents learn from every interaction. 74 MCP tools, Qdrant vector store, Memgraph knowledge graph with temporal edges, stigmergy coordination, 34 n8n consolidation workflows. Procedures, trajectories, and learnings accumulate into organizational intelligence.
Hierarchical context ensures agents maintain coherent behavior across sessions, projects, and teams. Auto-memory and intelligent compression prevent context loss.
Structured business rules, decision trees, SOPs, and edge-case catalogs. Agents query domain knowledge at decision points through MCP tools with full provenance tracking.
Twenty-two quality analyzers and fifteen security scanners run against every change, scored on a thousand-point scale.
27 integrated open-source tools powering 37 analyzers (22 quality + 15 security). Scan profiles run from a 30-second pre-commit pass to a full pre-release audit. Cryptographic attestation with Ed25519 signatures.
Behavioral anomaly detection, identity lifecycle management, memory integrity verification, and inter-agent collusion scoring. Gartner-aligned guardian agent oversight.
End-to-end data lineage, quality validation, pipeline observability, continuous PII classification, and financial reconciliation with calculation replay. Trace any output back to its source, prove mathematical integrity across system boundaries. Built for insurance regulatory requirements.
Cryptographically-chained audit trails, human attribution, and cost governance — the paperwork an auditor actually accepts.
Human attribution, cryptographically-chained immutable audit events, signed and versioned evidence packages, data subject rights router, human decision gates with signed receipts, incident tracking. Control-domain scoring for ISO 42001, EU AI Act, OWASP Agentic, SOC 2, ISO 27001, and GLBA.
Web portal for compliance officers, auditors, data subjects, and domain experts. Audit explorer, evidence packages, gate decisions, DSR management, model cards, regulatory reports. The missing surface that makes PRD 18 operable.
Per-interaction cost tracking, four-tier budget hierarchy (org/project/agent_class/agent_instance), semantic caching, prompt cache tracking, intelligent model routing (Haiku/Sonnet/Opus), and Cost Per Successful Outcome (CPSO) metric. Know what every agent costs, set limits, and maximize ROI.
Trust levels, data classification ceilings, audit trails, and LLM threat detection. Every agent action is logged, every permission is enforced, every decision is traceable.
Beyond cost tracking — task completion rates, time-to-resolution, first-pass success, rework frequency, and ROI attribution. Passive observation of existing system events, no new instrumentation.
Historical workload analysis drives adaptive model routing, cache pre-warming, cost forecasting with confidence intervals, and concurrency optimization. Statistical, not ML.
22 code quality analyzers. 15 security scanners. 6-stage enrichment. Mutation testing. Adversarial review. Cryptographic attestation. This is what production-grade AI development looks like.