Run a fleet of agents you can actually supervise
Evigauge
Agent operations · Use case
One agent is a demo. Forty agents calling each other is an operation, and it needs real supervision.
Agent operations
Fleets do not fail loudly. They drift — and supervision is what turns drift into something you can act on.
The situation
Research assistants, back-office automation, ticket triage and internal copilots — increasingly several agents handing work to each other.
Hand-offs are where fleets fail. One agent drifts, everything downstream degrades, and the symptom surfaces somewhere else.
Where the value leaks
Three costs land before anyone files a ticket. None of them shows up as a failure, which is exactly why they persist.
| Cost | Where it goes |
|---|---|
| TIME | The symptom is never where the cause is A failure surfaces at the last agent in the chain. Finding the one that drifted means walking the topology by hand. |
| COST | Token spend with no owner Per-agent consumption is invisible, so an agent burning budget on retries looks like one doing useful work. |
| RISK | Alerts nobody trusts One global threshold across agents doing different work fires constantly, and teams learn to ignore it. |
One answer, beat by beat
A single run, with the record Evigauge writes at each step and what that record is worth once it exists.
- 01
Task in· Run opened
The whole hand-off chain is under supervision from the first tick.
- 02
Planner· Hand-off map
What depends on what is a map, not a conversation.
- 03
Specialist agents· Per-agent tokens
The agent burning the budget has a name.
- 04
Critic· Drift flags
Drift is flagged where it starts, before downstream degrades.
- 05
Result out· Live topology
More of the business can move onto agents without adding blind spots.
What we bring to it
Each capability, what it does to the run, and the result the business can hold someone to.
| Offering | What it does | What you get |
|---|---|---|
| Fleet monitoring | Maps the live topology — which agents call which, hand-off volume, per-agent tokens and cache — read off-band at zero added request latency. | A bottleneck you can point at on a map, rather than describe in a meeting. |
| Performance | Learns a latency baseline per operation rather than one flat global number, and resolves flags when an agent recovers. | Alerts your team reads, because they mean something when they fire. |
| Reasoning | Reconstructs the decision path through the hand-offs that produced a given result. | The agent that caused the failure is identified, not inferred. |
What actually changes
Walk the topology by hand to find the drift
The drifting agent is flagged where it happens
Alerts fire on every slow operation
Thresholds learned per operation, flags auto-resolve
Fleet token spend is one undifferentiated bill
Consumption attributed per agent
Around the table
The question each officer is asked, and the answer this puts in their hands.
CTO
“Can we put more of the business on agents?”
Supervision scales with the fleet, so adding agents stops adding blind spots.
Head of Platform
“What is the blast radius when one agent drifts?”
The topology shows what depends on it before it degrades downstream.
Head of AI
“Which agent is burning the budget?”
Tokens and cache are attributed per agent, so the answer is a name.
Fleets do not fail loudly. They drift — and supervision is what turns drift into something you can act on.
Ready to see it on your own workload?
Bring one real run. We will show you what happened.