Executing and Monitoring Workflow Runs
How workflow runs execute node-by-node, how per-run spend caps prevent cost overruns, and how to observe, pause, cancel, and retry runs in real time.
AI agent security, identity, governance, cost, and the engineering behind the control plane.
How workflow runs execute node-by-node, how per-run spend caps prevent cost overruns, and how to observe, pause, cancel, and retry runs in real time.
A node-and-edge visual canvas lets you compose, version, and audit multi-step AI agent workflows before anything runs — catching gaps that code reviews miss.
Agents running on borrowed human credentials create accountability gaps and excess privilege. Learn why agent-native identity changes the security calculus.
Register, configure, version, and debug every AI agent in your fleet from a single governed control surface with full audit trails and per-agent access control.
Register every API consumer as a named Application with scoped credentials and per-agent access controls — so you can see and revoke what each can do.
Governed A2A communication makes every inter-agent call authenticated, scoped, and audited — with agent cards, least-privilege identity, and guardrails.
Turn implicit agent-to-resource links into policy-bound connections with rate limits, spend caps, trust gates, and guardrails enforced at dispatch.
Share AI agents with partner organizations under explicit policies — request caps, expiry, instant revocation — without handing over credentials.
How signed trust manifests let organizations share AI agents across boundaries without shared secrets—each delegation explicit, verifiable, and revocable.
Learn how agent trust scores combine behavioral signals, compliance state, and cryptographic attestations into an auditable dispatch gate for AI agents.
A practical FinOps loop for agentic AI: attribute token and tool costs, set multi-level budgets, trigger alerts early, and enforce hard spend limits.
Signed webhook verification, idempotent handlers, reconciliation, and a dunning state machine are the four layers that keep billing state correct.
Per-connection usage tracking attributes every AI request and dollar to its source, enabling accurate chargebacks, capacity planning, and anomaly detection.
How AI marketplaces track MRR, ARR, and per-agent earnings, and manage partner payouts — the outbound money flow that billing dashboards miss.
How Praesidia's budget policies enforce hard spend caps: scoped policies, graduated threshold actions, and reservation accounting that stops overruns.
A prepaid credit ledger with per-agent usage records gives you real-time visibility into AI spend and a hard gate that stops agents before they overspend.
How subscription plans, metered usage, invoices, and enterprise contracts fit together so AI token and tool costs map cleanly to billing.
How splitting user self-service from admin controls reduces the attack surface of an AI platform and keeps account hygiene manageable at scale.
Per-org security policies let tenants enforce password complexity, session timeouts, MFA mandates, and IP allow-lists — enforced server-side on every request.
Fine-grained RBAC and custom roles let AI operations teams enforce least privilege across agents, workflows, and security settings — without admin grants.
Teams add a functional access layer beneath org roles: scoping agents, enforcing per-team budgets, and integrating SCIM for automated provisioning.
How multi-tenant org isolation protects AI agents and data, with invite flows, role lifecycle, and layered enforcement that prevents cross-tenant data leakage.
SCIM 2.0 automates user lifecycle for AI platforms — collapsing the access-change window from days to minutes and enforcing token revocation on deprovision.
How enterprise SSO with SAML and OIDC maps IdP identities into org-scoped access for AI platforms — and why federated authentication matters for AI tooling.