AI agent observability is how Tragentics gives you a complete, durable record of every call your agents make — who called whom, when, the status, the latency, the bytes — while storing none of the payload. You see exactly what your fleet did, and we never hold a prompt, a response, or a byte of your data.
See everything your agents do — store nothing they say
Every call your agents make is logged the instant it happens — which agent called which, when, whether it worked, how long it took — and Tragentics records all of it without ever storing a word of the payload. You get the full picture of what your fleet is doing, and the platform never holds a prompt, a response, or a byte of your data.
That's AI agent observability the way it should work: complete, automatic, and content-blind. Most logging makes you choose — capture enough to debug, or keep sensitive data out of your logs. We don't make you choose. The record holds the metadata of every call; the contents stay yours.
You don't instrument anything to get it. Connect an agent and it's already being observed — every lane, every call, from the first one. No SDK, no switch to flip.
What AI agent observability means on Tragentics
AI agent observability on Tragentics means you can see, understand, and prove what every agent did — because every call writes its own record automatically, scoped to you, and ready to export. Not a sampling. Not the calls you remembered to wrap in logging. Every call, on every connection type, the moment it happens.
We do this better than a bolt-on logging stack for two reasons. It's automatic — there's nothing to instrument, no SDK to thread through your agents. And it's durable: writes are retried and idempotent, so a transient failure can't drop an event or double-count one. The trail you'll lean on in an audit is actually complete.
Observability isn't a nice-to-have — it's the gate to deploying agents at all. Leadership teams won't sign off on agents in billing, support, or sales without an audit trail they can stand behind. And the build-it-yourself answer falls apart fast: teams write everything to plaintext and then can't stitch a single agent run back together. AI agent observability is what turns "we think it worked" into "here's the record."
A full audit trail, without storing a single payload
Every call writes an AI agent audit log entry — the caller, the target, the time, the status, the HTTP code, the latency, and the request and response sizes in bytes. What it never writes is the content. Not the prompt, not the response, not the data in between.
Here's why that beats the logging most platforms ship. Capturing payloads into your logs feels thorough until you realize you've copied every sensitive prompt and customer record into a second system you now have to secure. We keep the AI agent audit log to metadata, so the trail tells you everything about the call and nothing you'd be afraid to export. You can see exactly how the proxy works — it routes the call without reading it, and logs the same way.
Plaintext payload logs are a breach you're keeping on purpose, and a compliance finding waiting to be written up. A content-blind AI agent audit log is safe to retain, safe to hand an auditor, and safe under HIPAA, SOX, or GDPR by construction — because the regulated data was never in it. That's AI agent observability that doesn't become its own liability.
Reconstruct any call from its trace ID
Every call Tragentics handles carries a trace ID, so you can pull a single agent interaction back together end to end — which agent called which, over which connection and protocol, whether it succeeded or fell back, and exactly how long each hop took. One ID, the whole story.
This is where homegrown logging quietly fails. Without a trace ID propagating across every call, your logs are a pile of disconnected lines you can't reassemble — noise, not a record. We stamp the ID on every call automatically and give you a trace explorer to search and inspect any one of them.
When something breaks across thousands of agents at 3am, "which agent did that, and what happened" has to be a single lookup, not a week of forensics. A trace-linked agent audit trail turns an incident into a query — the difference between AI agent observability you can act on and logs you can only apologize with.
Know which agents are alive — and how every call performs
Tragentics gives you real-time AI agent monitoring: which agents are alive, and how every call is performing. Each agent reports a live status — online, idle, or offline — through heartbeats and proxy activity, and the analytics surface turns the call stream into the numbers that matter: total calls, success rate, P95 latency, active connections, pool and schedule utilization, and fleet uptime.
This is AI agent monitoring without a second tool. You don't stand up an APM and wire your agents into it — status and performance come straight from the same call path that's already doing the routing, so what you see is the real traffic, not a sampled approximation. The docs on agent status and heartbeat and the analytics summary lay out the full picture.
One dead or degrading agent in a multi-agent flow silently breaks everything downstream of it — and in production you need to see that before your users do. AI agent monitoring that's built into the connection layer catches it the moment a heartbeat stops or a success rate slips.
An audit trail built for retention, scale, and real audits
The agent audit trail Tragentics keeps is built to survive a real audit. It's durable and tamper-resistant, kept under retention windows and built to scale, and scoped to your account by row-level security so no other tenant can ever see it — and it covers more than proxy calls: authentication decisions, agent lifecycle and configuration changes, credential rotation, and revocations all land in it too.
What makes it audit-grade rather than just "logs": an auditor doesn't want a flat text file you grep — they want a complete, tenant-isolated, queryable record they can trust wasn't quietly edited, and that they can export. That's what you hand them. Platform activity and security events sit in the same owner-scoped trail as the calls themselves.
The bar is no longer optional. NIST's AI Risk Management Framework and the AIUC-1 standard now require traceable, auditable agent activity, and a plaintext log won't clear it. An AI agent audit log that's durable, retained, and owner-scoped is the difference between passing the audit and rebuilding your logging the week before it.
Ship agents you can actually prove
Turn on nothing — Tragentics is already logging. Every call is accounted for, every payload stays private, and the AI agent audit log is ready for an auditor from the first request your agents make. You don't bolt observability on after something goes wrong; it's there before you connect the first agent.
That's the whole point of AI agent observability done right: you get to deploy agents into the work that actually matters — the regulated, the revenue-bearing, the customer-facing — because you can finally prove what they did. Everyone else is stuck choosing between agents they can't see and a plaintext log they can't defend. Tragentics hands you both halves: complete visibility, and not one byte of your data stored to get it.