Agentic AI tech stack selection

A stack you can leave without a rewrite

Agents that keep working when a model or vendor changes. Framework, models and hosting chosen on tests with your own data, with a written plan to switch any of them.

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30+ production deployments since 2017
Why stack choices age badly

Stacks picked from demos, on criteria nobody wrote down.

Lock-in, cost at volume and skills gaps surface a year later, when switching is expensive.

  1. 01

    Where it stallsThe stack is chosen after a convincing demo.

    What we do Criteria agreed and weighted before any demo.

  2. 02

    Where it stallsOptions compared on the vendor's own examples.

    What we do The shortlist is tested on your data.

  3. 03

    Where it stallsNobody plans how to leave.

    What we do Every layer gets a written exit path.

How we choose

Criteria first, then evidence from your data

Your criteria, tested by spikes on your own data.

  1. 0103

    Weighted criteria, then shortlists

    Weighted criteria rule options out before anyone falls for a demo.

    • Criteria weighted with your team
    • Shortlists for all five layers
    • Constraints that rule options out
  2. 0203

    Spike tests on your data

    Two or three options per layer run on your data, and the measurements decide.

    • Accuracy and cost per task
    • Latency and glue code measured
    • Hosted and open-weight models compared
    With Mixam's print-order agent, conversion to a final sale went from about 20% to about 40%.
  3. 0303

    A decision you can reverse

    The record names each layer's choice, the options rejected and why, and the trade-off you accept.

    • A decision record per layer
    • An exit path per layer
    • Spike code in your repositories
Engineering in the open

We help build what agentic AI runs on

Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.

Shaping agentic AI

The first agentic AI engineering company to join the Agentic AI Foundation

The Linux Foundation formed it around Anthropic's MCP, OpenAI's AGENTS.md and Block's goose. AWS, Google and Microsoft are platinum members. In its own words: “Together, we're shaping the future of agentic AI.”

Open source

We build open-source agent tooling that other engineering teams install

9M+ PyPI downloads
3,900+ GitHub stars
31 public repositories

Framework contributors

When the framework breaks, we fix it at the source

We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase.

Research

Our engineers and PhDs publish peer-reviewed research

Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.

Track record

Frontier AI teams now build on what we shipped first

Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day.

Case studies
All case studies
Engagement models

Ways to start

Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold.

Start with one workflow

We map one workflow with your experts and agree a success threshold before any build.

Best whenYou want the success test agreed before you commit to a build.

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  1. Engineering-led advisory Best whenYou need an engineering view before you commit to a build.
  2. Process redesign first Best whenYou know the process, not yet the agent's steps.
  3. Sovereign AI consulting Best whenData residency or model control decides the stack.
Layers we compare

The layers a stack decision covers

Typical options per layer. Your criteria pick the shortlist.

  1. Agent framework Compared on your data
  2. Model Swappable, behind an interface
  3. Retrieval Sized for volume and data location
    • pgvector
    • Qdrant
    • Milvus
  4. Observability Traces, evaluations, cost per task
    • Logfire
    • Langfuse
    • OpenTelemetry
  5. Your environment Your cloud or your region
    • Your cloud
    • Self-hosted
FAQ

Questions about agentic AI stack selection

What does agentic AI tech stack selection include? +
A choice for every layer an agent system depends on: the agent framework, the models, retrieval and the vector store, observability, and where the system is hosted. Each layer is chosen against criteria you agree up front, such as lock-in, cost per task, your team's skills, compliance and data residency, and shortlisted options are spike-tested on your data. You get a written decision per layer with the trade-offs and rejected options, an exit path for every layer, and the spike code in your repositories.
You contribute to Pydantic AI. Will you just recommend it? +
No. We recommend per use case. Our engineers contribute to Pydantic AI and Vstorm is an official Pydantic implementation partner, so we know where it fits and where it does not. LangGraph, LlamaIndex or a smaller set of plain libraries can be the better choice for a given use case, and the spike results and the decision record show the reasoning, so you can check it.
Hosted or open-weight models: how do you decide? +
On your use case and your constraints. Hosted models are quicker to start with and need little operations work. Open-weight models can run in your own environment, which matters for data residency, regulated data and cost at high volume, but your team has to run them. We test the shortlisted models on your tasks and keep the model behind an interface, so changing it later is planned work rather than a rewrite.
What is a spike test? +
A short, throwaway build that runs a shortlisted option on a slice of your real case: your documents, your tools, your edge cases. It measures what the criteria ask for, such as accuracy on your tasks, cost per task, latency and how much glue code the option needs. Spikes replace vendor benchmarks with evidence from your own data.
What is an exit path, and why write it down now? +
For each layer, the exit path says what you would switch to, what the switch would touch and roughly how much work it is. Writing it down while the decision is fresh keeps the option open: interfaces are drawn so the model, the vector store or the tracing tool can be replaced without rebuilding the agent. A vector store or embedding model change also means re-indexing, and the exit path prices that in.
We already have a stack. Can you review it instead? +
Yes. We run the same criteria against what you have, spike the alternatives only where a layer looks weak, and tell you which parts to keep. If an agent built on it has stalled, an AI agent audit is usually the better starting point.
Talk to the engineers

Bring the stack question you are stuck on

Bring your constraints and options; we say what we would test first, and why.

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