Threat Model: Over-Broad MCP Tool Scope
Over-broad MCP tool permissions give attackers an amplified attack surface. Learn the failure modes and control classes that shrink the blast radius.
MCP servers, workflows, observability, and the control-plane engineering behind it.
The authoritative reference for bringing MCP servers under governance: security model, authentication, tool scoping, rate limits, monitoring, and hardening.
Read the guide →Over-broad MCP tool permissions give attackers an amplified attack surface. Learn the failure modes and control classes that shrink the blast radius.
Design abuse-resistant rate limits for AI agents: choose the right unit, window shape, and enforcement scope to protect costs and downstream systems.
Gain full visibility into every MCP tool call an AI agent makes — with attribution, policy decisions, and cost data needed for security and compliance.
A practical guide to what AI agent observability must cover — cost, behavior, and policy compliance — and the key criteria for choosing the right tooling.
Evaluate MCP gateways on four criteria that actually matter: agent authentication, per-tool scoping, rate limits, and forensic audit logging.
HMAC signatures plus timestamp replay windows are the minimum bar for secure webhooks — here's why unsigned endpoints are dangerous and how to fix them.
Version AI agent workflows like code, diff changes between snapshots, and roll back safely when a new version causes regressions or runaway costs in production.
Compare pipeline, hub-and-spoke, and blackboard orchestration patterns for multi-agent AI — with security, cost, and auditability trade-offs for each.
How logs, metrics, and distributed traces apply to AI agents — what to instrument, where costs hide, and how to connect all three pillars for fast incident triage.
Request counts alone don't protect AI APIs. The layered controls — per-connection limits, spend caps, tool allow-lists, and trust gates — that actually work.
OAuth 2.1 vs API keys for MCP server auth: a practical comparison of security trade-offs, blast radius, and when to use each in production AI agent deployments.
Most MCP servers ship with no built-in authentication. Learn how to add identity verification, per-caller tool scoping, and bidirectional guardrails to production MCP deployments.
Grant AI agents the minimum MCP tool access they need — no more. Learn how allow-lists, per-tool rate limits, and policy gates prevent blast-radius breaches.
A practical MCP server security checklist covering authentication, tool-level authorization, rate limits, forensic logging, and monitoring for agentic AI systems.
MCP gives AI agents a standard way to call external tools and retrieve context. Learn what it is, how it works, and what security controls a production deployment requires.
Every platform action is an API call. Learn how Praesidia's OpenAPI-described surface lets you automate governance, integrate tooling, and extend the platform.
The platform admin console gives super-admins cross-tenant visibility, DLQ triage, two-person governance controls, and GDPR erasure on a separate access plane.
A persistent, authenticated WebSocket stream replaces polling for agent tasks, workflow runs, and budget alerts — and what safe multi-tenant fan-out requires.
How to design liveness and readiness probes for AI services — what to check, how to avoid false positives, and what a production health surface should look like.
Stream AI agent events to your own systems and forward security signals to a SIEM — so agent activity is visible in the tooling your team already uses.
Issue, scope, and rotate organization API keys to give each integration only the access it needs — and limit blast radius when a credential is exposed.
Turn scattered user requests into ranked roadmap signal with a built-in feedback board that supports voting, moderation, and multi-tenant visibility.
Route AI agent budget alerts, guardrail violations, and task failures to Slack and other channels with a reliable, tenant-isolated dispatcher pattern.
Build a compliant email opt-out system with enforced suppression lists, per-category preferences, and automatic bounce handling — and keep your sender reputation intact.
Reliable transactional email for AI platforms: how consistent templates, authenticated sending, and delivery safeguards keep security and billing flows intact.
Browser push notifications deliver agent failures and budget alerts to operators the moment they happen — no open tab or email check required.
How a purpose-built in-app notification system keeps AI platform operators informed of critical agent events and alerts without noise or alert fatigue.
How to capture, aggregate, and act on authentication events in your AI platform so credential attacks surface in minutes, not days.
Charts, dashboards, and cost breakdowns that make AI agent spend legible — from real-time KPIs to anomaly detection and per-team attribution.
Praesidia exposes a standard Prometheus metrics endpoint so you can monitor AI agent task throughput, latency, and spend using the tools your team already runs.
Search across agents, tasks, connections, workflows, and audit logs from a single entry point — find any resource in your AI estate instantly.
Saved views let AI operations teams restore any dashboard state in one click — cutting investigation setup time and reducing filter errors under pressure.
Set measurable SLOs for task success rate, latency, and agent availability — then alert before users notice. A practical guide to SLOs for AI agent deployments.
Turn AI activity into board-ready governance reports covering usage, cost, and risk — with scheduled delivery and export for compliance teams.
How an AI operations dashboard correlates agent counts, spend, trust scores, and security events in one view — and what to do when the numbers look wrong.
Go beyond basic dashboards: model comparison, cost-per-team allocation, anomaly detection, and compliance analytics for AI operations teams.
How a per-interaction event model powers AI agent dashboards, cost attribution, and forensic investigation — without additional collection infrastructure.
Per-org feature overrides let you enable a capability for one tenant, observe real behavior, and expand gradually — without touching your deployment pipeline.
Plan-based feature flags gate capabilities by subscription tier while per-org overrides enable safe canary rollouts — no deployment pipeline changes required.
How real-time collaboration on AI workflow canvases works: CRDTs for conflict-free edits, durable working documents, presence, and per-edit authorization.
Register your own LLM provider keys in one encrypted registry, route workloads to the right model, and eliminate key sprawl — without platform lock-in.
Register MCP servers centrally, enforce per-tool permissions and rate limits, and log every invocation for audit — governance that unmanaged connections lack.
Workflow templates let teams deploy proven agent pipeline patterns in one click — spreading best practices and simplifying governance across the organization.
Turn a plain-language description into a reviewable multi-agent workflow draft in seconds. Learn how AI generation works and where human review stays essential.
Learn the three ways to start an AI workflow — cron schedules, signed webhooks, and internal platform events — and which trigger fits each operational pattern.
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.
Register every API consumer as a named Application with scoped credentials and per-agent access controls — so you always know what each integration can do and can revoke it instantly.