The control layer for AI agents

Run AI agents on your own infrastructure.

aamp is the orchestration layer between AI agents and your real systems. Models change every quarter. Token prices fall every quarter. What doesn't commoditise is the layer that decides which model runs, enforces what it can touch, records what it did, and stops it before it burns the budget.

Build the layer once. Swap models forever.

01 · Use cases

What teams use aamp for.

We want to start with RAG…

Runs entirely inside your perimeter. Documents are ingested and retrieved host-side. Nothing is sent to a third party.

Our legacy stack needs AI…

Connects to systems with no API. ERP, CRM, finance, flat files — nothing gets replaced.

We need agents we can defend…

Every action leaves a trace. Prompt, retrieval, tool call, output and token spend, logged per agent.

LLM · RAG · agents · workflows · scheduling — one control layer.
02.1 · Why: Sovereignty

Self-hosted. Your infrastructure, your jurisdiction.

Runs on your infrastructure, in your jurisdiction. No third party to trust.

All three answers are yours. aamp runs on your infrastructure, in your jurisdiction. There is no third-party retention policy to verify, because there is no third party. Every prompt, retrieval, tool call and output is logged host-side as a by-product of running the system.

aamp · sovereignty
Screenshot / illustration
02.2 · Why: Cost

Tiered routing, loop budgets, per-token attribution.

Routing, loop caps and per-agent budgets, built into the architecture. Not bolted on after the first invoice.

An agent hits the model ten to twenty times to finish one task. Reasoning tokens bill as output. A query priced in cents becomes tens of dollars per outcome. aamp caps the loop, routes the routine work to smaller models, and attributes every token to an agent and a workflow.

aamp · cost
Screenshot / illustration
02.3 · Why: Model freedom

Any OpenAI-compatible model, bound per agent.

Any OpenAI-compatible model, bound per agent — local open weights, hosted frontier, or both.

The landscape moved twice in six weeks this summer. One frontier model went dark for nineteen days under export controls. Anything architected around a single provider is a bet on someone else’s roadmap. aamp binds models per agent, per workflow, per capability — local open weights, hosted frontier, or both.

aamp · model freedom
Screenshot / illustration
03 · How it works

One console for every control plane.

aamp · operator console
The aamp operator console — agents, ingress, helpers, knowledge, workflows, firewalls, jobs and logs in one navigation
ConsoleAgents, ingress, helpers, knowledge, workflows, firewalls, jobs and logs — one host-side surface, not a dashboard bolted onto someone else's cloud.
04 · Use it when

Use aamp when…

01

…you're giving AI agents a safe sandbox to build and run in.

02

…you're connecting AI to legacy systems with no API — ERP, CRM, finance.

03

…you're in a regulated sector and every AI action has to be explainable.

04

…you're done watching agents burn $4 in tokens to save 15 minutes.

05 · How we differ

Automation platforms connect apps. aamp governs agents.

The orchestration layer is its own category. Here is where aamp sits in it.

Dimension aamp n8n make.com Direct APIs
Runs on your infrastructureYesSelf-host optionNoNo
Agent sandboxing and audit trailYesNoNoNo
Tiered routing and loop capsYesNoNoNo
Model-agnostic, local or hostedYesConnector-levelConnector-levelNo
Cost stays flat as you automate moreYesNoNoNo

See the full comparison →

Try aamp in the sandbox.

Free sandbox · Self-host in minutes · Talk to us

05 · Partners

Partner with aamp.

Our partners offer a range of professional services — expertise and technology capabilities to help our customers both make the first step and enrich their solutions.

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