Observability & governance for AI agents
Run AI agents you can observe, govern, and trust in production
Your agent has been live for a few weeks. Requests are flowing. Dashboards look green. But a small, painful percentage of users are still getting confidently wrong answers, and your logs show nothing broken. At the same time, security and architecture teams are asking hard questions about what these agents can access, what they’re allowed to do, and how you’ll keep costs under control as usage grows. In our upcoming three-part MongoDB webinar series, we’ll walk through a practical playbook to make agentic AI systems observable in production.orm you can actually operate over time.
Part 1: Observability for AI Agents
September 9, 11:00–12:00 p.m. ET
Monitoring tells you whether your service is up; observability helps you understand why agents behave the way they do, especially when they fail silently.
You’ll learn how to:
Distinguish observability from monitoring for agentic systems and explain why that distinction matters more for agents than for traditional services.
Identify the four major failure categories in agent workflows (reasoning, tools, context and data, and infrastructure) and see how they can collapse into the same outward symptom: incorrect output.
Use logs, metrics, and traces together to investigate agent behavior, debug multi-step workflows, and support decision auditing and compliance.
Part 2: Governance for AI Agents
September 16, 11:00–12:00 p.m. ET
Agents access data, invoke tools, and take actions, often autonomously. Governance is how you decide what they’re allowed to do, under whose authority, and with what evidence when something goes wrong.
You’ll learn how to:
Explain governance as a policy-and-accountability framework that coordinates safety, security, compliance, and observability rather than sitting alongside them as a separate concept.
Map the governance control surface across external actions, tool use, prompts, context and retrieval, memory, model behavior, and identity boundaries.
Define autonomy boundaries for agents: what they may decide on their own, when human approval is required, and what must remain human-only.
Apply least-privilege thinking to agent identities, data access, and tool permissions, avoiding legacy “root access everywhere” anti-patterns.
Design runtime guardrails—allowlists/denylists, action gating, rate limits, and data handling rules—and map MongoDB capabilities like Atlas RBAC, auditing, encryption, App Services rules, and Vector Search access scoping into those control points.
Part 3: Evaluating an Agentic AI Platform
September 23, 11:00–12:00 p.m. ET
Most enterprises get stuck in the demo-to-production gap: impressive agent prototypes that stall at security review, blow past token budgets, or fragment into framework-specific silos across teams.
This session gives you a structured way to evaluate agentic platforms, including the MongoDB Agentic Platform, across four key dimensions:
Platform architecture:
Understand the Golden Rule: Build agents with frameworks, operate agents with platforms.
Security and observability:
Ask the core validation questions of any platform.
Operational and economic sustainability:
Assess scalability, reliability, data locality, and the “Four Scaling Cost Drivers” (network egress, HA topologies, storage multipliers, split architectures) so unit costs stay flat as workloads grow.
Decoupled flexibility:
Evaluate how platforms decouple execution choices (frameworks, LLMs, runtimes) from a single governance and observability layer, and why that 18‑month hedge against vendor and cloud lock-in matters.
Even if you can’t attend live, register once to get access to all three recordings on demand and share them with your team. You’ll end up with a checklist you can use to compare agentic platforms and justify your recommendations to architecture, security, and executive stakeholders.
