Sentinel sits at the intersection of LLM tracing and regulated-enterprise infrastructure. This document clarifies where it fits, what it does not do, and where it is headed. For the strategic framing — evidence infrastructure for the regulated AI era, the Trace / Attest / Audit / Comply formula, the four modules — see docs/vision.md.
| Category | Examples | What they do | Sentinel |
|---|---|---|---|
| LLMOps / Tracing | Langfuse, LangSmith, Phoenix, Helicone | Debug LLM calls, latency/cost metrics, prompt iteration, evals | Complementary — does not trace individual LLM calls or provide dev debugging; LangFuse ships a Sentinel-compatible integration |
| AI Agent Frameworks | LangGraph, CrewAI, AutoGen, Semantic Kernel | Build agent workflows and state machines | Complementary — does not build agents, wraps them for policy + evidence |
| AI agent governance (closed beta) | Dome Systems | Runtime control layer for enterprise agent fleets | Adjacent — Dome Systems is US-incorporated, structurally subject to US jurisdictional regimes including the CLOUD Act; Sentinel is the EU-jurisdictional alternative |
| AI agent governance (OSS) | Microsoft Agent Governance Toolkit | OWASP Agentic Top-10 coverage for LangChain / CrewAI / Dify | Complementary for Microsoft-first shops — MS AGT is US-incorporated; Sentinel is the answer when EU-jurisdictional evidence is required |
| Sovereignty-first cybersecurity | Cylake | Hardware + software for sovereign agent environments at national scale | Adjacent — Cylake is US-incorporated, positioned around sovereignty-themed cybersecurity messaging; Sentinel leads with Provability instead |
| Native cloud governance | Azure AI Foundry, AWS Bedrock Guardrails, GCP Vertex AI | Deeply cloud-integrated policy and observability | Multi-cloud clasp — excellent per-cloud; loses value in multi-cloud / on-premise / air-gapped scenarios |
| Enterprise Platforms | Proprietary full-stack AI platforms with built-in decision layers | End-to-end AI deployment with vendor-controlled decision recording | Alternative — auditor-grade evidence layer for deployments where proprietary platforms are jurisdictionally excluded |
| GRC platforms | Sastrify, Enactia, Kovrr, ADOGRC | Inventory, risk classification, and management of AI systems | Partner candidates, not competitors — runtime layer (Sentinel) vs. inventory layer (GRC) |
| Prompt Management | Langfuse Prompt Mgmt, PromptLayer, Humanloop | Version prompts, A/B test, human-in-loop iteration | No — does not manage prompts or do prompt engineering |
| Eval Frameworks | DeepEval, RAGAS, UpTrain | LLM output quality scoring, RAG evaluation | No — does not score LLM quality or run evals |
| Observability | OpenTelemetry, Grafana, Datadog | General infrastructure monitoring, distributed tracing | Complementary — OTel export shipped as sentinel-kernel[otel]; native JSON/NDJSON remains the primary format |
Relationship key: Complementary = works alongside. Upstream = Sentinel wraps or builds on it. Downstream = consumes Sentinel output. Adjacent = different problem, no overlap. Designed for = Sentinel addresses this category's gap.
Sentinel occupies a category with a distinctive shape: evidence infrastructure for the regulated AI era. It produces a structured, append-only, auditor-grade artefact for every autonomous decision — capturing what was decided, under which policy, by which system, and under whose jurisdiction.
This is complementary to observability and LLMOps, not competing with them. A team can use Langfuse for prompt debugging and Sentinel for compliance records. They answer different questions:
| Question | Answered by |
|---|---|
| Why is my agent slow? | LLMOps / observability |
| Is this prompt performing well? | Eval frameworks |
| What did my agent decide, and can I prove it to a regulator? | Sentinel |
Sentinel wraps agent function calls. It does not require replacing your existing stack.
| If you use | Sentinel integrates via |
|---|---|
| LangChain | sentinel-kernel[langchain] — shipped callback handler |
| CrewAI / AutoGen | sentinel-kernel[crewai] / [autogen] — shipped task hooks |
| Haystack | sentinel-kernel[haystack] — shipped component wrapper |
| LangFuse | sentinel-kernel[langfuse] — shipped sovereignty panel |
| OpenTelemetry | sentinel-kernel[otel] — shipped span exporter |
| Prometheus | sentinel-kernel[prometheus] — shipped textfile collector |
| FastAPI / Django / Jupyter | [fastapi] / [django] / [jupyter] — shipped middlewares |
| Any LLM provider | Model metadata recorded in trace (set explicitly) |
| OPA / Rego policies | LocalRegoEvaluator — in-process, no network |
| Python rule functions | SimpleRuleEvaluator — zero dependencies |
LangGraph and PydanticAI integrations are explicitly postponed — see
the Intentionally postponed section of the v3.1.0 CHANGELOG entry.
Agent → sentinel.trace() → SQLite / NDJSON trace
↓ (optional)
OTEL → Langfuse / Grafana
Traces are always written to local sovereign storage first. OpenTelemetry
export is shipped as an optional extra (sentinel-kernel[otel]) and
runs downstream of the local write — the local record is the source of
truth and cannot be gated by the exporter.
Observability tools record what happened. Sentinel records what was decided — and attaches policy evaluation, jurisdictional metadata, and human override records that make the trace legally meaningful under EU AI Act Article 12.
The distinction matters in regulated environments:
- An observability trace says "the model returned this output in 312ms."
- A Sentinel trace says "this agent decided to approve this request, under this policy, which returned ALLOW, with inputs hashed as SHA-256, stored on EU-DE infrastructure, at this timestamp, with this schema version."
The second is what a regulator, auditor, or court needs.
Manifesto Chapter VIII names the gap explicitly: no other solution simultaneously offers model-spanning consistency, EU jurisdiction for evidence, open source with exit capability, and cryptographic provability in a regulation-mapped format.
Sentinel sits precisely in this intersection. LLMOps tools watch performance, not decisions. Cloud governance is locked to one cloud. Closed-beta governance platforms are US-incorporated. Sovereignty-first cybersecurity leads with the wrong noun. GRC platforms manage AI systems, they do not sit in the runtime path of a decision.
That is the category Sentinel occupies. See docs/vision.md for the full strategic framing.