AI agents are finally moving past cute demos and into actual production workflows. With AWS AgentCore, teams can build agents that write tickets, call APIs, deploy infrastructure, invoke external tools, and make changes faster than any human operator ever could.
That’s powerful, but it also introduces a brand-new operational and security surface. And here’s the uncomfortable truth: most organizations have no idea what their agents are actually doing.
Agentic AI isn’t magic. It’s a privileged automation layer that needs the same observability you expect from Kubernetes, Lambda, or any other critical service. If you don’t get the logs out of it, you’re blind.
This is why we built the Sumo Logic Amazon Bedrock AgentCore App. Not to add more dashboards for dashboard’s sake, but to give teams the visibility required to run agentic workflows safely and predictably. The app serves as a blueprint for something bigger: what effective AI logging should look like.
This blog isn’t a product walkthrough. It’s a practical argument for why logging your AI matters, and how AgentCore shows the path forward.
AI agents are the new privileged service accounts
Except they make decisions autonomously.
AgentCore agents can trigger actions across your stack, such as creating cloud resources, integrating with SaaS apps, running code, fetching data, or updating systems based solely on their instructions and context. If an intern had this level of access and you weren’t logging what they did, you’d shut that down in five minutes. When an AI agent does it, many teams shrug it off as “just AI being AI.”
That’s not sustainable. If an agent can affect production, you need full visibility into everything it touches.
The five log categories you need to monitor in every AI system
Below are five log categories you should be monitoring in every AI system. AgentCore exposes them natively through CloudWatch and CloudTrail, and the Sumo Logic app visualizes them. But these categories apply to any agentic AI platform.
Runtime logs: What the agent actually did
This includes execution traces, operations, errors, retries, outputs, and step-level activity. This is your incident timeline and audit trail. If something breaks, these logs answer the question: “What did the agent do in the moments leading up to this?”
Gateway logs: What external systems the agent touched
Most of the “dangerous” parts of agentic AI involve interacting with external APIs and services. Gateway logs show exactly which calls were made, where, and with what results. This is where you catch misconfigurations or repeated failures.
Memory logs: What the agent stored or retrieved
Memory is essentially a dynamic knowledge base controlled by the model. Writes, reads, and updates should always be logged so you can track how your agents are evolving their context.
Built-in tools (Browser and code interpreter)
These are the high-risk surfaces. If the agent is visiting URLs, executing code, or running scripts, you want every one of those actions logged. The Sumo Logic dashboards for these tools give you visibility into operations that could easily slip past traditional security controls.
Identity and access logs via CloudTrail
This is how you track who invoked an agent, who modified its configuration, who added new tools, and who changed permissions. Governance and compliance depend on this.
What you learn from the Sumo Logic AgentCore app
The app gives you an opinionated view of what matters most when monitoring agentic systems.
- Overview dashboard: This shows invocation patterns, error rates, latency distributions, and top agents and tools. This is where you spot behavioral drift or early signs of instability.
- Runtime dashboard: Here, you’ll find step-level activity for each agent run, similar to APM traces. It helps you understand how an agent reasons through tasks and where failures occur.
- Gateway dashboard: This exposes external API usage and integration behavior. This is critical for understanding how your agents interact with the rest of your environment.
- Built-in tools dashboards: These track browser and code execution activity. These are your “high-risk automation” views.
- Identity dashboard: This shows how agents are being invoked and modified. This helps teams catch unauthorized changes or suspicious invocation patterns.
Even if you’re not using AgentCore yet, the structure of these dashboards shows the baseline requirements for observability in any AI platform.
Why logging AI matters
Agentic AI will break things. It will misunderstand instructions. It will take actions with high confidence that a human operator would reconsider. That’s the reality of autonomous systems.
The problem isn’t that agents occasionally make mistakes. The problem is when they make mistakes in environments where no one is watching.
Logging is how you establish guardrails, explain incidents, detect misuse, enforce governance, track drift, tune behavior, and maintain operational trust in systems that act autonomously.
AI without logs is unmanageable. AI with logs is just another part of the stack.
What teams should be doing right now
You don’t need a massive AI deployment to start building the right observability posture. Follow these steps to start monitoring your AI systems.
- Turn on logging for runtime, gateway, memory, built-in tools, and identity events.
- Centralize it in Sumo Logic so you’re not correlating across scattered log groups.
- Baseline normal behavior. These systems behave differently from traditional services.
- Add alerts for suspicious patterns such as repeated gateway failures, unexpected browser activity, new tool attachments, or unusual invocation spikes.
Treat AI agents like privileged service accounts with automation capabilities. Because that’s what they are.
Closing thought
AI isn’t inherently safe or unsafe. It’s observable or not.
AgentCore gives teams a structured way to use agentic AI, while Sumo Logic provides the visibility to run it responsibly. When you instrument your agents like you instrument your infrastructure, you get reliability, accountability, and trust.
When you don’t, you get a black box that can deploy infrastructure at 2 a.m. with no paper trail.
Logging isn’t optional. It’s the foundation of trusted AI operations.
