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§ Agent · Fastly

The Fastly data agent that acts the way you would.

It watches your Fastly billing and traffic data alongside your infrastructure and product metrics, on a schedule you set or whenever fresh data lands. When something needs attention, it tells you, or handles it the way you would.

D
DefiniteAPP9:14 AM · #engineering-alerts
⚠️ Fastly bandwidth cost up 41% month-over-month; 2 services account for 78% of the increase

June invoice is projecting $8,400 over your $12,200/mo baseline, driven by cache-miss rates climbing on api-prod and static-assets since the June 9 deploy. Bandwidth line items are 3.2x their 30-day average.

Review & approve Dismiss
Fastly Invoice + Invoice Line Items + Service Metrics · joined to deploy log · audit log

How an agent works

An agent watches one thing and acts on it. Not a workflow, just a standing watch that usually does nothing and acts the moment it should.

◄ repeats on the schedule you set ►

You stay in control

An agent does what you'd do, and only what you've authorized.

The same trusted numbers

It acts on the same governed metrics as your dashboards, and every action is logged and traceable.

You approve anything that writes

It alerts and recommends on its own; anything that changes data is yours to approve.

Try it on a test channel first

Point a new agent at a throwaway channel and watch its judgment before it touches anything real.

No false alarms

It remembers what it already flagged and waits before acting again, so it won't alert you about the same thing twice.

What you can put an agent on

Cost to UsageACROSS YOUR SOURCES

Tie your Fastly invoice to the traffic that drove it

It joins your Fastly invoice line items to service-level traffic metrics and your infrastructure data, so you can see which services, endpoints, or deploys drove the bill. You stop guessing why the invoice jumped and start tracing it to the commit.

InvoiceInvoice Line ItemService MetricService
Cache Efficiency

Catch cache-miss spikes before they become cost spikes

When your cache-hit ratio drops or miss rate breaks its trend on any service, it tells you which service moved, when the shift started, and how much extra origin traffic it is generating. You find out in hours, not when the invoice arrives.

Service MetricService
Error Rate

Flag 5xx and 4xx surges with the service and timeline

When error-rate status codes climb on a service, it surfaces which service, the error distribution, and when it started, and routes it to the right person before users start filing tickets.

Service MetricService
Custom

Run any Python it needs to get the job done

Beyond alerts and write-backs, an agent can run arbitrary Python, so it can do whatever the task actually requires: call an API, kick off a job, reshape the data, or wire into your own tooling. The action space is yours to define.

Why not just build it yourself?

You could rig one of these with a cron job and a Slack webhook in an afternoon. The watching is the easy part. Here's what you'd own forever, and don't, here:

  • The cross-source join: not one tool's data, but it reconciled against the rest of your stack
  • A trusted, consistent metric: the same number your dashboards use
  • The investigation into why, when something fires
  • A full audit trail of everything it did
  • The upkeep, when the schema drifts or the script breaks at 2am

The data it works from

Every Fastly object, modeled and query-ready the moment you connect.

Invoice
revenue_financeengagement
Invoice Line Item
revenue_financesupport
Service Metric
revenue_financemarketing
Service
revenue_financemarketing

It runs on your real Fastly account (mid-cycle invoices, WAF noise, test services and all), not a tidy demo.

Where it acts

Slack

A message in the channel you choose, with the context and a button to act on it.

Email

A summary in the inbox of the people who need to see it.

Webhook

A payload to your own systems, to wire the agent into whatever you already run.

Warehouse write-back

A flag written back to your warehouse for everything downstream to pick up.

Hand off to Fi

Kick the question to Fi to investigate the why and propose the fix.

MCP

Expose it to your own agents and tools over MCP, and drive it from your stack.

Run it in your own VPC or fully self-hosted. Everything it does is pure SQL and Python you can inspect.

Build your agents with Fi

Fi is your AI analyst. It helps you build and customize everything in Definite, including the agents that watch and act.

Fi

Your AI analyst. Ask questions in plain English, and let it help you build and customize everything in Definite, including your agents.

Meet Fi →

Agents

The watchers and actors. Once you've built one, it runs on its own, keeping an eye on what matters and acting the way you would.

Autonomous agents →

Get started

  1. 1Connect Fastly, and the sources it needs to reconcile against. Synced and modeled in an afternoon.
  2. 2See the numbers tie out to what you already trust.
  3. 3Put an agent on one thing you can't afford to miss. Fi helps you build it.
§ FAQ

Common questions

You set the schedule, and it also re-checks whenever fresh Fastly data lands. Each agent watches the one thing you point it at, nothing else.
It alerts and recommends on its own. Anything that writes, whether to a tool, your warehouse, or a customer, is yours to approve. You can also point a new agent at a test channel first and watch its judgment before it touches anything real.
When something fires, it can hand off to Fi to investigate, drilling into the data it has across your connected sources to find what's behind the move, and showing its work.
Those show you Fastly metrics when you go look, scoped to Fastly alone. This watches continuously, joins Fastly cost and traffic data to your deploys and infrastructure, and acts on the shift the moment it happens, so you trace the bill spike to the commit before the invoice closes.

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