Agents are the most unpredictable workload you have ever operated: the cost moves every request, the behaviour can change based on what they read, and an entirely new class of attack targets them. 24Observe gives AI and platform teams the two things that prototype-grade tooling does not — production observability (cost, latency, behaviour by model and agent) and real security (dedicated detections for injection, tool abuse, and runaway loops) — from the OpenTelemetry your stack already emits, self-hostable when your prompts must stay home.
The gap between a demo that works and an agent you can run in production is enormous, and most of it is exactly the unglamorous operational ground that AI tooling has not yet covered: cost, behaviour, and security under real traffic.
Cost is the first shock. In the prototype it was a rounding error; in production it is a variable you do not control, because an agent's spend per request depends on how much it read, how many times it looped, which model it picked, and how long it reasoned. That means your bill can multiply with no deploy and no traffic spike — just an agent that started taking a longer path — and the first time most teams discover this is the invoice. You cannot manage what you cannot attribute, and generic infrastructure monitoring has no concept of a token.
Behaviour is the second. Traditional software does what its code says; an agent does what it is persuaded to do by the content it reads, and it can decide to call a tool more often, reach for a different capability, or enter a loop it never exits. These are first-order operational events with no stack trace, invisible to monitoring that counts requests and statuses, and they are the difference between an agent that is quietly fine and one that is quietly broken or being abused.
Security is the third, and the least served. The moment you gave a model the ability to read untrusted input and call tools, you took on prompt injection, tool-loop abuse, and tool-protocol attacks — risks your existing security stack was never written for and largely cannot see. Pointed at agent traffic, a conventional tool sees a stream of model and tool calls and has no notion of what an injection looks like. The attack surface moved; most defences did not follow.
24Observe is built for the operating job, not the prototype. It reads the OpenTelemetry your stack already emits, surfaces the GenAI-specific facts that matter, and turns them into production answers: what is each agent costing, how is its behaviour drifting, and — on the very same spans — is any of it an attack the analyst should investigate. And because that telemetry is some of the most sensitive data you hold, you can run the whole thing self-hosted and keep every prompt inside your perimeter.
The hard part of agents was never the demo. It’s running them when the cost moves every request, the behaviour drifts, and a new class of attack is aimed straight at them.
One integration — the OpenTelemetry you already emit — answers the cost question, the behaviour question, and the security question at once, because they all read from the same spans.
Spend by model and by agent, honestly totalled, with unpriced calls flagged — find the budget-burner before the invoice. Observability →
Latency, error rate, and call volume by agent and operation — the loop, the retry storm, the overnight 4× surfaced as events.
Dedicated detections for prompt injection, tool loops, cost abuse, sensitive tool use, and tool-protocol attacks. Agent security →
An agent threat opens an incident the analyst investigates and returns a verdict for — not a flag you have to chase.
Agent spans sit beside your logs, metrics, and traces, so you can tell the model from the database when an agent slows. Tracing →
Open source, with an identical contract on your own infrastructure — your prompts and completions never have to leave your network. Self-host →
If you are a platform team enabling others to build with AI, your job is to make shipping an agent safe by default. 24Observe lets you give each team a clear view of their own agents' cost, performance, and security while you keep the platform-wide picture and set the guardrails centrally — redaction patterns, the detection packs that watch for abuse, the budgets that become alerts when an agent overspends. The teams move fast; you keep the floor under them.
Monitors, detections, alerts, and budgets-as-thresholds are all in the API, so the guardrails for a new agent can be provisioned in the same pull request that ships it. You are not hand-configuring observability per team in a console; you are encoding a safe-by-default template that every new agent inherits. That is how a platform team scales AI enablement without becoming a bottleneck.
AI teams change models constantly — a cheaper one here, a more capable one there, several in an ensemble. A tool tied to one provider's API fragments under that churn into a dashboard per vendor. Because 24Observe keys off the open standard, every agent lands in one consistently-costed view no matter which model or provider it called, so a model swap is a line on a chart rather than a migration of your observability.
The agents you are watching do not run in isolation — they sit on infrastructure that also needs uptime, logs, metrics, on-call, and incident response, and those are already here. When an agent misbehaves it opens an incident routed to whoever is on call, investigated by the analyst, exactly like any other signal. You start at the AI edge and the rest of the reliability and security platform is already underneath you when you need it.
A single production incident that is simultaneously a cost problem and a security problem — and how one stream of telemetry catches both.
A customer-facing support agent has run cleanly for weeks. No deploy, no traffic spike, nothing a generic dashboard would flag — but overnight its token spend quadruples. For an AI team without agent-aware tooling, this is invisible until the monthly bill, followed by the miserable archaeology of working out which of a dozen agents did it, over thirty days, and why.
Here it shows up the next morning, attributed. The agents view shows the support agent's spend spiking sharply against its own baseline, and the totals are honest because unpriceable calls are flagged rather than hidden. The behaviour signals point at the cause: call volume has jumped without a matching rise in the work the agent is supposed to be doing — the fingerprint of a loop that is not converging. Because prompts and tool calls are searchable, you go from "this agent is suddenly expensive" to "here is the exact sequence it keeps repeating" in one step.
And this is where observing and securing become the same act. A runaway 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 spend spike in the engineering view is also a security incident, and the analyst investigates it: was this a bug in the agent's stopping condition, or did a crafted input make it loop? It traces the input the agent read, the behaviour that followed, and returns a verdict with the evidence cited.
The verdict says the loop was triggered by a malicious input designed to exhaust the agent's budget, and the recommended response — cap the loop, quarantine the input pattern, tighten the stopping condition — is proposed for a human to approve, not executed blindly. Your team caps the loop and ships the fix, and spend returns to baseline by the afternoon: a same-day catch of something that, untracked, would have been a month of silent overspend and an unnoticed attack.
The thing to notice is that you ran one integration and got both answers. You did not buy an observability tool to spot the cost and a separate security tool to spot the attack and then try to correlate them; the same telemetry, read two ways, surfaced the spend and the threat as one event. For an AI team, that unification is the difference between operating agents and merely hoping they behave.
That unification matters more as your agent estate grows. One agent you can watch by hand; ten agents calling several models, reading untrusted input, and spending real money are a system, and a system needs instrumentation that understands it rather than a scatter of dashboards bolted on after the fact. Because cost, behaviour, and security all read from the one stream of telemetry you already emit, adding the eleventh agent does not add an eleventh integration — it inherits the same visibility and the same guardrails automatically. The platform scales with your ambitions for AI instead of becoming the thing that limits them.
Cost, behaviour, and security from the OpenTelemetry you already emit — one integration, self-hostable, with the rest of the reliability and security platform underneath when you need it.