Cost of agents

Most of what agents cost, they waste.

Before you attribute the bill, it is worth making it smaller. Nearly all avoidable agent spend comes from three places, and all three are governance settings rather than model choices.

See FinOps for AI Agents
Usage analytics showing tool call volume falling as the surface is narrowed

Where it goes

Three ways to pay for nothing.

None of these show up as a line item. They show up as a total that is larger than the work would suggest.

  • Choosing between a hundred tools

    Every tool an agent can see costs tokens to read and attention to discard. Most of a bloated context is capability that was never going to be used.

  • Guessing, then retrying

    An agent that picks the wrong tool does not stop. It tries again, and each attempt bills exactly like a successful one.

  • Doing it the long way

    Without a playbook, a model reconstructs your process from first principles on every run — and takes a more expensive route than the one your team would take.

What to change

Four settings, in the order that matters.

Each one reduces the work the model has to do. The third is a hard stop rather than an improvement — it is what stops a bad run becoming an expensive one.

  1. Narrow

    Show fewer tools

    Spaces expose only what an identity should see. A smaller surface is a shorter context and a smaller chance of choosing wrong.

  2. Instruct

    Carry the process in a Skill

    The sequence and the judgement calls come from a Skill rather than from the model improvising, so fewer steps are spent working out what to do.

  3. Bound

    Cap the retry loop

    Rate limits stop a failing agent from billing indefinitely, at the gateway, before anything downstream notices.

  4. See

    Find the expensive patterns

    Usage per space, per agent and per tool shows which work is costly and which is simply repeated — the first is a decision, the second is a bug.

The same task

One run, two ways of paying for it.

The work is identical. The difference is how much the model has to figure out before it can start.

Everything visible, nothing written down

  • Seventy tools in context, most irrelevant to the task
  • A wrong tool chosen, then retried
  • The process reconstructed from scratch on every run
  • Failed attempts billed exactly like successful ones
  • Spend visible only as a monthly total

Narrowed and instructed

  • Only the tools this identity should see
  • The sequence carried by a Skill, not inferred
  • Retry loops capped at the gateway
  • Failures counted alongside successes
  • Usage broken down per space, agent and tool

What you get back

A number that means something.

Cutting waste changes the total. Measuring it properly changes what you do next.

  • Cost per completed task, not per call

    A call is not a unit of value. What matters is what a finished piece of work costs, including everything that failed on the way to it.

  • Failures counted, not hidden

    Denied, rate-limited and errored calls appear alongside successes, so the real denominator is visible.

  • Attribution when you need it

    Once waste is under control, the question becomes who should pay for the rest. That is what the FinOps work is for.

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Common Questions

Quick answers about Palma.ai's enterprise MCP platform

What is Palma.ai in one sentence?

Palma.ai is the enterprise governance layer for MCP — it gives every person and agent a single governed connector carrying the tools and Skills they're entitled to, enforces policies on the actual arguments of a call, pauses high-risk actions for approval, and records everything in a tamper-evident audit trail.

What does MCP governance mean?

Deciding which person may use which tool, with which arguments, with whose approval — and being able to prove it afterwards. MCP itself covers how a client authenticates to a server and how a tool is described and called; it does not decide which person may use which tool, hold a risky call for a human, or keep the record an auditor asks for. Palma adds that layer: one governed connector per person, assigned by identity-provider group, carrying the tools and Skills they are entitled to into whichever assistant they already use.

Does my team have to set up MCP servers themselves?

No. Connectors are assigned by IdP group through Entra or Okta, so a joiner gets theirs on day one and a leaver loses it the moment the group changes. Every MCP server your team approves arrives through that same connector — no per-user install, no config files, no credentials sitting on a laptop.

What's a Skill, and why does it matter?

A tool is a verb — "send an email". A Skill is the playbook that tells an agent when and how to use the verbs it already has: how your team actually closes the books, runs an incident review, or qualifies a lead. Skills are versioned, scanned before they're served, and scoped like any other piece of enterprise software — so your best operator's process reaches everyone else's agent.

Does it work with the AI clients we already use?

Yes — everything is served over MCP, so the same connector, Skills and policies follow the person into whichever assistant they open, whether that's Claude, ChatGPT, Copilot, Cursor or something else. Switching tools doesn't mean re-approving, re-installing or re-auditing anything.

How do we prove what an agent actually did?

Every tool call is attributed to the person it was done for, the agent that did it, and the application it ran in — with the arguments, result, duration and cost. The audit trail is tamper-evident and verifiable offline with your own key, so your auditor doesn't have to take our word for it, and it streams to the SIEM you already run.

How is Palma.ai deployed — SaaS, on-prem, VPC?

Palma.ai is designed for enterprise environments: typically VPC or on-prem, including fully air-gapped, depending on your regulatory and security needs. The MCP layer and governance plane run on your infrastructure, so sensitive business data doesn't have to move into multi-tenant SaaS. We can also host it for you if you prefer.