Now generally available Cost Intelligence

Connect agent spend
to business outcomes

Map agent spend back to the agents, activities and workflows that generated it – so the question stops being "how much are we spending on AI?" and becomes "is this spend doing useful work?"

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Real-time attribution · No agent code changes · Same record as your security data

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Get out ahead of agent spend creep

In summer 2026, two things happened at once. AI provider pricing shifted, and the workforce found their stride with agents – long coding sessions, research agents left to grind overnight, workflows that quietly fan out into a dozen sub-agents.

Promotional credits kept the cost line flat while consumption climbed underneath, and native dashboards report totals, not attribution. The first accurate picture of demand arrived as an invoice.

50% of a bank's monthly AI budget got consumed in a single day because an agent got stuck in an endless tool loop
$000s in tokens are often needlessly spent on an exercise because a frontier model was used for a task that didn't need one
$1m in model-routing waste accumulated because tasks are routed to more expensive models than they require

A combined view of risk

Spend creep has become a crucial part of agent risk. Assess every component of agent risk in a single lens.

Security

What sensitive data can this agent reach, which tools and credentials does it hold, what happens when it connects to a malicious skill?

Operational

Where might an agent take an action that breaks a process, corrupts a system of record, or brings a workflow to a halt?

Financial

What is this agent consuming, is the work worth it, and who is accountable for the number? Can we minimize rogue agents and identify effective ones?

A cost number on its own is not intelligence

Conventional telemetry describes consumption – which models ran, how many tokens they used, what that came to. Every one of those facts is about how AI was used up. None is about what AI produced.

Record one · consumption

What was used up

Models invoked: 4
Tokens: 1.84m
Cache hit rate: 31%
Cost: $93.00

On its own: a line item to be defended.

Record two · production

What was produced

Objective: Refactor billing module
Outcome: 2 PRs merged
Owner: Payments platform
Workflow: pr-review-agent

Together: a data point in an investment decision.

What you have today

Scattered platform invoices, no agent-level view

Anthropic monthly invoice

Total usage: $14,200
By user: Not available
By agent: Not available

GitHub Copilot admin

Seats: 142 active
Total cost: $5,680/mo
Per-agent cost: Not available

+ Foundry, HubSpot Breeze, Codex…

Each with its own dashboard

Platform-level bills, no agent attribution
No cross-platform view; manual stitching
Evidence assembled by hand the week before the audit.

Leads to simplistic, unproductive conclusions: 'We need to cut the highest spenders'

What Geordie Cost Intelligence tells you

Agent-level spend + behavioral context, all platforms

Help desk

Code review

Onboarding

QA triage

Ops alerts

Model mix

Opus on trivial tasks

Help desk: switch to Haiku
Cache efficiency

38% utilization

Onboarding reloads context
Task pattern

Repeated prompts

Could automate system-to-system
Spend anomaly

Loop detected

Onboarding agent stuck 4h

Agent-level cost across every platform, in real time
Behavioral context: model, caching, task type
Connects spend to what the agent actually did

Actionable, nuanced conclusions. In this case: 'code review and QA triage agents are delivering strong ROAI. Cut help desk's model tier. Fix onboarding's caching. Automate ops alerts entirely.'

Henry Comfort

Co-founder and CEO @ Geordie

Everything you need to understand, and report on, agent spend

All of it built from agent activity, not estimated from invoices.

Multi-dimensional breakdown of spend and agent work

Drill from an organization-wide total down through platform, team, user, model, agent or workflow.

Anthropic

OpenAI

Bedrock

Cursor

Cross-platform spend aggregation

AI spend from every connected platform in one view, attributed to the agents and the work that generated it – not only the activity that happens to route through a single gateway or model router.

Token-efficiency mapping

Spend and volume mapped per agent, so the high-value workhorse and the runaway loop are distinguishable at a glance.

Cache-utilization insight

Per-agent caching efficiency as a first-class metric, flagging which agents are under-caching and what that costs.

31% hit rate · $14k/mo recoverable

Cost anomaly detection

Infinite tool loops, oversized model routing and sessions burning tokens with little output – surfaced by the same behavioral engine that identifies security risk.

Every total traces back to the work underneath it

Per-agent spend for the current month, with the work each agent produced and the resulting cost per unit of output.
Agent	Owner	Outcome	Spend	Per unit
pr-review-agent	Payments platform	412 PRs merged	$96,410	$234
claims-triage	Claims ops	18,240 adjudicated	$74,880	$4.10
market-research	Strategy	Loop · no output	$55,120	—
doc-summarizer	Legal	9,110 reviewed	$35,640	$3.91

"Expensive" and "wasteful" stop being synonyms once the third column exists.

Financial risk, security risk, and accountability risk, all in the same picture

Connect one platform and we will show you where your agent spend is going, what it produced, and which agents are quietly the largest consumers in your organization.

Questions, answered

Still unsure? Ask us anything – we answer in a day.

No. Geordie attaches to the agent's own execution path through the harness it already runs inside, so spend from every connected platform is captured – not only the activity that happens to pass through one router. Coverage isn't limited to a single tool type, and there's no chokepoint to scale.

Cost is captured by the same lifecycle-hook instrumentation that records agent behavior, as the activity happens, rather than estimated from a bill afterwards. There's no step where a log is correlated back to an invoice, which is what usually puts weeks between the activity that produced the spend and the number that reveals it.

In practice they're the same problem: ungoverned agents operating without oversight. Wasteful token consumption comes up in roughly every other first customer meeting we have, often before security concerns do. The same behavioral understanding that surfaces security risk explains the financial consequence, because the instrumentation producing both is identical.

Developer agents running side projects on company tokens, duplicate agents doing the same work across departments, agents that keep running when they should have stopped, and cost scaling that compounds silently. Each is a consequence of agent behavior, which is why the engine that identifies security risk is the one that finds it.

No. It's the same understanding applied to a different question – the instrumentation that observes agent behavior for security purposes also captures the operational data explaining financial consequences. Nothing extra deploys, and spend arrives attached to the same record as the risk posture of the agent that generated it.

It tends to widen the group. Cost gives the security team a credible reason to bring IT, AI and Finance into the agent governance conversation as natural allies, and it lets a CISO present risk posture and operating cost to the board in one view rather than assembling it from dashboards that don't talk to each other.

Yes. An early adopter of Geordie's cost intelligence capability gained attribution of agent spend back to specific agents, actions and owners for the first time. Token consumption that had been an unattributed line item became a governable, accountable dimension of agent operations – and a reason to bring finance into the conversation.

Connect agent spend
to business outcomes

Trusted by forward thinking teams

Get out ahead of agent spend creep