When your AI application breaks, nobody knows where to look.
Is the agent the problem or the symptom? Causely answers that before your engineers open a single dashboard.
Your agentic app is degrading. Your observability stack sees symptoms everywhere.
Latency spikes. Error rates climb. Token costs explode. The engineer on call opens their observability stack and sees the agent retrying, tool calls piling up, a downstream service looking slow.
They don't know where to start because they don't know what caused what. Is the agent misbehaving? Is it waiting on a slow database? Did a prompt change trigger a retry loop? Did an upstream service degrade and the agent is just the most visible victim?
Without causal context, every hypothesis is a separate investigation thread. Engineers waste the first 30–45 minutes just establishing whether the agent is the cause, a victim, or a red herring. Meanwhile the agent is bleeding inference tokens on retries.
Every minute of wrong diagnosis costs you twice.
Engineering time
30–45 minutes to establish the causal chain across agent and infrastructure layers. That's before anyone starts fixing anything.
Token bleed
While engineers investigate, a degraded agent retrying against a slow upstream service burns inference tokens on every failed attempt. Faster diagnosis directly cuts inference spend.
Your agentic apps run on the same infrastructure as everything else. Causely already models that infrastructure.
Causely maps your agentic application into the same causal model as your services, databases, and infrastructure. When something degrades, a single causal query returns the full chain, agent → service → database, with a deterministic root cause.
Service Map
You know whether the agent is the cause or the symptom before you open a dashboard. You fix the right thing. The retry loop stops. The token bleed stops.
This isn't a new capability. It's the same causal model Causely already runs for your infrastructure, extended to cover a new class of entities.
Causal Chain
What this looks like in practice.
Without Causely:
Agent throws errors → engineer suspects LLM model issue
→ queries LLM provider status → no incident
→ checks agent logs → retries look high
→ pulls upstream service metrics → order-svc looks slow
→ queries database → Postgres connection pool at 94%
→ establishes causal chain: Postgres → order-svc → agent
With Causely:
causely.entity_health("agent-id")
→ ROOT CAUSE: postgres-primary connection pool exhausted
→ CAUSAL PATH: postgres-primary → order-svc → agent
→ OWNER: platform-team (#db-oncall)
Single query, full causal chain
agent → service → database, with a deterministic root cause rather than a list of hypotheses to work through.
Cause vs. symptom, immediately
Know before you investigate whether the agent is the problem or waiting on something broken underneath it.
Token bleed stops faster
Faster diagnosis means fewer retry cycles. Every minute saved in diagnosis is inference spend you don't burn.
Your agentic apps run on the same infrastructure Causely already models.
More use cases
Reduce Observability Costs
Most of your observability bill is data you never needed to store.
Causely collapses agent fan-out into a single causal query, 48% fewer tokens and fewer billable API calls.
Read →Build an AI SRE
Your AI SRE works in demos but not in your production environment.
Causely provides the causal model of your system that makes it accurate.
Read →CONNECT BUSINESS OUTCOMES
The business sees the drop before engineering knows what caused it.
Causely connects business metrics to the infrastructure causing them.
Read →