An LLM agent is the strangest thing you have ever run in production: it costs a different amount every time, it can change its own behaviour based on what it reads, and a single bad loop can quietly burn a budget overnight. 24Observe gives you the vital signs — token spend by model and by agent, latency, errors, and call volume — from the OpenTelemetry you already emit, on the same timeline as the rest of your system. The observability your agents need, with the security they were missing one click away.
Traditional software is predictable in the ways that matter for operating it: the same input costs the same, does the same, and takes about the same time. Agents are none of those things, and that breaks the instincts a decade of monitoring taught you.
Start with cost. A conventional service has a roughly fixed unit economics; an agent's cost per request is a variable that depends on how much it read, how many times it looped, which model it chose, and how long its reasoning ran. That means your spend can multiply without a single deploy, without a traffic spike, without anything your existing monitoring would flag — just an agent that started taking a longer path through its work. The first time most teams learn this is the invoice, which is the single worst place to learn it.
Then there is behaviour. Normal software does what its code says; an agent does what it is persuaded to do by the content it encounters, and it can decide to call a tool more times, reach for a different capability, or enter a loop it does not exit. These are not bugs in the traditional sense — there is no stack trace — and they are invisible to a monitoring stack that counts requests and statuses without understanding that a "request" here is a model deciding what to do next. The agent that quietly tripled its call volume overnight is a first-order operational event, and most tooling sails right past it.
And the telemetry itself is unfamiliar. The interesting facts about an agent live in attributes a generic logs or metrics tool does not know to surface — which model, how many tokens, what the prompt and completion were, which tool was called and what it returned. Pointed at agent traffic, conventional tooling shows you a stream of opaque calls and leaves the GenAI-specific meaning on the floor.
24Observe is built to treat agents as the genuinely new kind of workload they are. It reads the standard GenAI telemetry your stack already emits, surfaces the attributes that matter, and turns them into the three answers every team running agents actually needs: what is this costing, how is it performing, and — one click away on the same spans — is any of it an attack. You stop flying blind on the most unpredictable thing in your production estate.
An agent can multiply your spend and change its own behaviour without a single deploy. Monitoring that counts requests and statuses was never going to catch that.
If your stack already emits OpenTelemetry — OpenLLMetry, OpenInference, the Vercel AI SDK, or your own spans — you are two environment variables away from a full agent dashboard. No new SDK, no schema to define, no agent to install.
export OTEL_EXPORTER_OTLP_ENDPOINT="https://api.24observe.com/api/v1/otlp"
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer obs_<your_token>"Any span carrying standard GenAI attributes appears instantly, attributed and costed. Because it keys off the open telemetry standard rather than a single provider's API, agents calling different models and providers all land in the same coherent view — so swapping a model or running several at once does not fragment your picture into a dashboard per provider. The fastest integration is the one where you send what you already have, and that is exactly what this is.
The point is not a wall of charts; it is the three questions that actually decide whether your agents are healthy, answered the moment telemetry lands.
Token spend broken down by model and by agent — exact when your SDK reports it, your negotiated rates when you set them, list pricing otherwise. Find the budget-burner before the invoice does.
Calls that cannot be priced are flagged as unpriced, never silently counted as zero — because a cost view that quietly undercounts is worse than none at all.
p95 latency, error rate, and call volume by agent and operation — the retry storm and the slow tool surfaced, not buried in raw spans.
The agent that tripled its calls overnight or started taking a longer path through its work shows up as the operational event it is.
Prompts, completions, and tool calls are searchable, so you can go from "this agent is expensive" to "here is exactly what it was doing" in one step.
The same spans power threat detection — prompt injection, runaway loops, sensitive tool use. AI-agent security →
Evaluation and tracing platforms tell you whether your agent gave a good answer; traditional security tools never see your agent traffic at all. 24Observe does both from one stream — the cost-and-performance view your engineers want and the threat view your security team needs — because the telemetry that explains an agent is the same telemetry that defends it. You instrument once and you are not running, paying for, and reconciling two separate tools that happen to look at the same data.
Your agent spans do not live in an AI-only silo. They sit beside your application logs, metrics, and traces, so when an agent slows down you can see in one place whether the model is the bottleneck or the database it called is — without exporting from an AI tool and grepping in a logging tool. AI is part of your system now, and its telemetry belongs next to the rest of it, not quarantined. See tracing.
Agent traffic is some of the most sensitive data you hold — prompts and completions can contain anything your users typed and anything your systems returned. 24Observe is open source and self-hostable with an identical contract, so you can run the whole thing on your own infrastructure and keep every prompt inside your network. Redaction strips secrets before storage regardless. You are never forced to ship your most sensitive AI data to a third party just to understand it.
Start with agent observability, and when you need uptime, logs, metrics, on-call, and incident response, they are already here — not a migration away. The agent that misbehaves can open an incident the analyst investigates, routed to whoever is on call, exactly like any other signal. You are not buying a point tool you will outgrow; you are starting at the edge of a platform.
The exact failure mode that does not exist in traditional software — and how agent-aware observability turns it from an invoice shock into a same-day catch.
A support agent has been running happily for weeks. No deploy goes out, no traffic spike hits, nothing in a conventional dashboard so much as twitches. But overnight its token spend quadruples. In a stack with no agent-aware view, this is completely invisible until the end of the billing period, when someone notices the number is four times what it should be and begins the miserable archaeology of working out which of a dozen agents, over thirty days, did it.
With cost attributed by agent, the story is visible the next morning. The agents view shows the support agent's spend has spiked sharply against its own recent baseline, and the totals are honest — calls that could not be priced are flagged rather than hidden, so the spike is real and not an artefact. You do not have to suspect the spend exists; you can see exactly which agent, and by how much.
The performance signals point at the cause. The same agent's call volume has jumped without a matching rise in the work it is supposed to be doing — a strong fingerprint of a loop that is not converging. Because prompts, completions, and tool calls are searchable, you go from "this agent is suddenly expensive" to "here is the exact sequence it keeps repeating" in a single step, and you can see the input that started sending it round in circles.
This is also where observability hands off to security without a context switch. A runaway tool loop is not only a cost problem; it can be the symptom of an attack — adversarial input deliberately driving the agent in circles — and the AI-agent detections watch for exactly that signature on the very same spans. So the same event that showed up as a spend spike in the engineering view can open a security incident the analyst investigates, and you find out not just that the agent looped but whether it was made to.
You cap the loop, fix the input handling, and the spend drops back to baseline by the afternoon — a same-day catch of a problem that, untracked, would have run for a month and arrived as a bill. Nothing in traditional monitoring would have surfaced it, because nothing in traditional monitoring understands that an agent's cost and behaviour are variables it controls itself. Watching agents as the new kind of workload they are is the whole difference.
Point OpenTelemetry at one endpoint and see cost, latency, and behaviour by model and agent in five minutes — on the same timeline as the rest of your system, with security on the very same spans.