Burning tokens?Your agents need context

Stop paying for missing context, unreadable docs, and invisible spend.

AI Engineer, powered by the Context Graph.

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Moneris logo
Siemens logo
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No spec, no context, so agents start guessing

An agent working alone in a small project barely needs context. Drop it into a real codebase, and what it needs is scattered across other repos, other teammates, contracts nobody wrote down. Without a map, it doesn’t fail. It guesses, and every guess is tokens spent.

That’s what Postman’s Context Graph fixes: one queryable map of every spec, collection, and connected repo, so the agent asks what’s real before it writes a line, instead of grepping around and hoping. Skip the guesswork, skip the retries, get the right answer on the first try.

Postman Context Graph screenshot showing agents pulling scoped context across a real codebase
ElevenLabs llms.txt served by Fern — a structured, agent-readable index of docs with instructions for AI agents and an MCP server endpoint

Traditional docs burn tokens unnecessarily

Most documentation is still written for humans, not agents, so an agent has to parse dense HTML pages just to answer questions a real spec would answer directly. When you browse docs like this with agents (like Claude, ChatGPT, or Cursor), it costs 5.6× more tokens than a spec, with lower accuracy.

Fern builds docs and SDKs straight from the spec, so they stay accurate and ship as structured, agent-readable text instead of HTML pages built for people to click through.

Fragmented AI access and visibility

AI costs grow quickly, but most organizations lack real-time visibility into who is consuming tokens, which LLMs are driving spend, and where budgets are being exceeded.

Fabric Gateway meters every token in real time across teams, users, LLMs, and routes, so you can set budgets, enforce limits, and catch overruns before the invoice does.

Postman Fabric Gateway analytics dashboard metering AI traffic in real time — total requests, error rate, P95 latency, active consumers, and per-protocol traffic across teams and LLMs
Astro AI Insights dashboard tracking real-time spend, requests, and tokens by agent, model, and team — ranking which agents are worth the spend

You don't know which agents are worth the spend

Run more than a handful of agents, and cost tracking breaks down fast. A $10 task and a $10,000 one show up identically on the invoice, so nobody can say which agents are earning their keep and which are quietly burning budget on jobs a cheaper model could’ve handled.

Astro AI tracks real-time spend and performance by agent, model, and team in one registry, so you can see exactly which agents are earning their keep and double down there.

Products

The Postman Agentic Stack

Postman Passport

Passport

Access management

Access management for humans and agents.

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Postman Fabric

Fabric

Agent-native gateway

An AI-native gateway with support for AI, MCP, and APIs.

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Astro AI

Astro AI

Agent operating system

An agent operating system to run and control agents.

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Fern

Fern

Docs, SDKs & CLIs

Agent and developer experience, covering docs, SDKs, and CLIs.

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Postman

Postman

The AI-native API platform

The foundation that ties together the API Catalog, the Context Graph, and the AI Engineer.

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RESEARCH

Go deeper into agents

Score your public APIs’ agent readiness

See how well Claude, ChatGPT, and Cursor can find, read, and build with your APIs.

Try it now →

APIFlow-Bench: the enterprise survival test

Postman scored 19 models on whether an agent can finish a real API workflow end to end, not just make one call.

Read the post →

The Agentic OS

Astro AI's account of running families of agents at scale, with token economics as an engineering discipline you can see and steer.

Read the post →

Talk to a Postman expert

A Postman expert can help map where you burn tokens across your SDLC. You'll learn what's driving cost, what to fix first, and what you'd save.