Observability Sees the Past. Your Network Needs Someone to Act on the Present

If you work in network operations, financial services, or any environment running at enterprise scale, you have invested heavily in observability (Dashboards, alerts, correlation rules, etc.) with Datadog, Splunk, Dynatrace, or something equivalent. The tools are mature, the market is crowded, and most teams have genuinely good visibility into what their systems are doing.

Despite this, incidents continue to escalate, and remediation still arrives after the damage is done.

The gap is not visibility; it is time. Observability tools are built around a fundamental assumption: that a human will see the alert, understand the context, and decide what to do. That assumption made sense when systems ran at human pace, but it is increasingly a problem when they do not.

By the time a dashboard surfaces an anomaly, the event that caused it has already happened. The network is already congested, the fraudulent transaction has already cleared, or the SLA has already been breached. You are looking at a record of something that went wrong, not a window to prevent it.

Most teams respond by tightening alert thresholds and adding more rules. At enterprise scale, with millions of events per second and sub-millisecond decision windows, this approach is becoming increasingly unfit for purpose.

The problem AI has made considerably worse

The observability vendors have noticed this limitation and are responding with AI. Anomaly detection, automated root cause analysis, natural language querying of log data, and AI-generated runbooks. These are genuine improvements to the investigation experience.

But they solve the wrong problem faster. An AI-enhanced observability tool tells you what went wrong more quickly. It does not prevent the thing from going wrong in the first place.

And then there is the agentic AI problem, which is distinct and considerably more significant.

Enterprises are deploying AI agents to automate operational workflows, such as network remediation, fraud detection, service orchestration, and customer care. These agents do not work the way humans do. A task that generated five database operations when a human did it, now generates 50 to 100 when an agent does it. Traffic patterns shift from steady and predictable to bursty, parallel, and relentless. Agents do not tolerate stale data the way humans do, and an outdated record can lead to a wrong decision delivered with complete confidence.

The conventional response to this is to add cache layers, spin up more read replicas, and bolt on a connection pooler, which all buy time but introduce new failure modes. AWS distinguished engineer Marc Brooker documented this precisely: cache-dependent architectures have two operating modes, working and catastrophically broken, and the transition between them is rarely gradual. Agent traffic, which is bursty, parallel, and aggressive on retries, is exactly the stress that triggers the bad mode.

So you now have AI agents hitting your infrastructure harder than it was designed for, reasoning from data that may be stale, and producing confident wrong answers at millisecond speed. Your observability stack will tell you this happened. It will not stop it from happening.

What an action layer actually does

Better observation is no longer an acceptable solution. What’s needed is a system that can take a signal, evaluate it against current state, make a deterministic decision, and enforce it before the operational moment has passed.

That is a different architectural problem from observability, and it requires different infrastructure to solve.

Observability tools are optimized for throughput and query flexibility. They ingest everything, index it, and make it searchable. That is the right design for investigation. It is the wrong design for real-time enforcement, because by the time you have queried the data, correlated the signals, and triggered a response, the window has closed.

An action layer needs three properties that observability tools were never built to provide simultaneously.

The combination of all three simultaneously is genuinely rare. Google’s Spanner, built by the world’s most resourced distributed database team, publishes 10ms reads and 50ms writes. Most observability-adjacent tooling operates in the seconds-to-minutes range for anything requiring stateful evaluation. The architectural gap is not incremental. It is structural.

The combination of all three simultaneously is genuinely rare. Google’s Spanner, built by the world’s most resourced distributed database team, publishes 10ms reads and 50ms writes. Most observability-adjacent tooling operates in the seconds-to-minutes range for anything requiring stateful evaluation. The architectural gap is not incremental. It is structural.

What this looks like in practice

Consider a network operations center handling congestion events. The observability stack detects the problem, alerts fire, and dashboards update. Engineers begin the investigation, and the root cause is identified. A remediation is proposed, reviewed, and executed. The process works and can take a matter of minutes.

At 2 am, however, with an on-call engineer half asleep, it takes longer. At scale, with hundreds of concurrent events, the queue of unresolved alerts grows faster than engineers can clear it. And when AI agents are involved in the remediation loop, the latency of the data those agents are reasoning from matters enormously. An agent acting on network state from 30 seconds ago may make a remediation decision that worsens the situation it was trying to resolve.

This is exactly what an action layer changes. For the same congestion event, the system immediately (in <10ms) evaluates it against current network state. 90 percent of cases will match known patterns, so the appropriate response (rerouting, throttling, resource reallocation) will be enforced without human involvement and without waiting for a query to return. For the 10 percent that require interpretation, it escalates with full context: what triggered this, what the current state is, and what has worked in similar situations before. The system has done the deterministic work, meaning the human can make a better decision faster.

Detect AND enforce. Not just alert.

The distinction matters enough to be worth stating plainly. 

Observability detects. An action layer enforces. These are not the same operation, adding AI to an observability stack does not close the gap. It makes the detection faster and more accurate, which is invaluable. But if the architecture cannot act on what it detects, in the same transaction, before the operational moment has passed, faster detection just means you see the problem more clearly before it completes. 

For architects evaluating this space, the questions worth asking are: 

• At what latency does your current stack make an enforcement decision (not an alert), and at what percentile?
• When an AI agent in your system makes a recommendation, what is the state of the data it reasoned from? How old is it?
• When something goes wrong, can you produce a complete chain showing what data was used, what decision was made, and why?
• What happens to your current stack when agent-driven traffic multiplies your database load by 10 to 20 times? Have you sized for that?

These are not hypothetical questions for 2027. Databricks paid approximately one billion dollars for a database company in 2025 because its telemetry showed that over 80 percent of databases were being provisioned by AI agents rather than human developers. OpenAI’s PostgreSQL load grew tenfold in twelve months. Citi projects the server CPU market will reach $132 billion by 2030, driven primarily by agentic AI workloads. The workload shift is underway, and the infrastructure implications are hitting production environments today.

Where Volt fits

Volt is not an observability replacement. It complements your existing stack, taking the signals those tools generate and acting on them before it’s too late.

The architecture is straightforward in concept. Observability tools continue to do what they do well: ingest everything, surface patterns, and alert humans to situations that need attention. Volt handles the deterministic enforcement path — the decisions that happen too fast for human intervention, that need to be consistent under load, and that need to be auditable after the fact.

For AI agents specifically, Volt provides two things. First, authoritative real-time state via MCP tools for structured queries that agents call during their reasoning process to get current, ACID-consistent answers to specific operational questions, rather than querying stale batch data. Second, deterministic evaluation of agent recommendations before anything executes. This means the same conditions always produce the same outcome, with a complete record of what the agent queried, what it was told, what it recommended, and what was decided.

The result is an architecture where your observability tools see everything, your agents reason from an accurate current state, and your enforcement layer acts before the moment has passed.

See it in action

The observability gap is easiest to see in a demo. We can show you how AI agents query Volt for real-time state, and what the complete decision chain looks like.

Request a demo at demo.voltactivedata.com