Agent actions
Capture the traces, events, logs, and metrics your agents emit — a structured record of what your fleet actually does in production.
PUVINOISE™ shows whether your AI systems and agents behave as intended at runtime — observing behaviour, understanding intent, forecasting likely outcomes, and supporting governed recovery across a multi-tenant control plane built for production.
PUVINOISE™ turns raw agent activity into governed outcomes — each layer building on the one before it.
Capture the traces, events, logs, and metrics your agents emit — a structured record of what your fleet actually does in production.
Sessions, decisions, tool calls, and state changes become high-fidelity behaviour signals you can reason about — not scattered stderr.
Correlate behaviour across your fleet. Surface meaningful deviation, drift, anomalies, and policy gaps — and see what is likely to happen next.
Governed policies and recovery close the loop — with audit trails, approvals, and human overrides.
Operational situation, runtime controls, and execution context for agent fleets.
The Command Centre answers the operator's core questions: what is running, what changed, and what requires intervention. Situation views aggregate fleet state without ad hoc dashboards, and runtime controls respect execution context — who triggered an action, under which policy, in which tenant.
Detect drift, anomalies, and policy gaps on governed behaviour surfaces.
Agents fail quietly — drift in tool use, gradual policy erosion, anomalous sequences that classic uptime metrics miss. Behaviour intelligence consumes telemetry and behaviour signals from instrumented agents to highlight what diverged from expected patterns, in language security, agent ops, and engineering all share.
MTTR-minded workflows and recovery lifecycle visibility for agent Runtime Cases.
Agent Runtime Cases are how production agent failures are tracked and recovered. PUVINOISE™ connects detection, triage, mitigation, and post-case learning — with recovery intelligence that helps leaders reason about MTTR and lifecycle stage, not vanity counts.
Recovery runs under the policies your teams set, with audit trails and human overrides — so it stays trustworthy at fleet scale.
Define how the platform may respond. Policies are explicit and reviewable.
When meaningful deviation is detected, recovery runs under your policies — with approval where you require it, and an audit trail throughout.
Track MTTR, lifecycle stage, and recovery success across the fleet, with correlation back to originating behaviour signals.
Multi-tenancy is not a sidebar feature — it is how enterprises onboard teams on one platform with tenant-scoped data and views. Detailed architecture is covered during security review.
Tenant-scoped
Legend: — tenant boundary · → data flow
Buyer-trust surfaces linked from the product journey — validate fit before commercial close.
Reference scenarios from SDK emit to governed recovery — no invented metrics.
Case studies →Five-dimension evaluation rubric to score on your fleet.
Benchmarks →Security, Privacy, OpenTelemetry, Roadmap, and Documentation.
Trust Center →Healthcare, fintech, edtech, B2B SaaS, enterprise B2B, and operations share the same layers — observe, understand, govern, recover — with industry-shaped risk language. Dedicated hubs live under Solutions.
Audit trails, policy-aware tool use, and MTTR when clinical or ops agents drift.
Healthcare solutions →Policy evidence and governed recovery when payment-adjacent agents fail in production.
Fintech solutions →Multi-org tenancy and safe behaviour signals when learners and staff are in the path.
EdTech solutions →Per-customer agent fleets on a shared control plane — tenant scoping without operational silos.
B2B SaaS solutions →Cross-team agents with one situation story from signal to recovery.
Enterprise B2B solutions →Shared-service agents with Runtime Case discipline and recovery intelligence leadership can track.
Operations solutions →Short, factual answers for teams evaluating behaviour runtime intelligence — written for humans and readable by assistants.
PUVINOISE™ is a behaviour runtime intelligence platform for AI agent fleets. It observes live agent behaviour, correlates signals into operational context, and supports governed recovery across a multi-tenant control plane.
Agent failures appear while agents are running — tool errors, policy skips, and cascading retries. Runtime signal, policy, and recovery on the same path shorten time-to-detect, time-to-decide, and time-to-recover.
No. PUVINOISE™ attaches to Python agents at runtime and is framework- and provider-agnostic. You keep your frameworks, models, and hosts; the control plane governs behaviour without a stack migration.
Teams that run AI agents in production — across healthcare, fintech, edtech, B2B SaaS, and operations — and need multi-tenant observation, behaviour intelligence, Runtime Case workflows, and recovery metrics leadership can track.
No. Industry pages are lenses on the same platform: observability, governance, multi-tenant control plane, and recovery. Risk language changes; the runtime spine does not.
Pricing is published on the Pricing page: Free to start, Contact Sales for Growth, Pro and Enterprise. Engineering engagements are scoped separately — start from Pricing for product plans, or Contact for an Engineering Consultation. Use Benchmarks to score fit during evaluation.
Walk through Command Centre, behaviour intelligence, and Runtime Case workflows with our team — or review Pricing and Benchmarks first.