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Why we joined the MACH Alliance Agent Ecosystem
The move from single AI-powered assistants to systems of agents that work together is happening as we speak. Those agents don’t all come from one vendor, they come from a CRM you deployed years ago, data platforms you migrated to last year, and AI model providers you’re evaluating today, and even agents your own team built to connect different systems. And getting them to interoperate, to work together safely, efficiently, and compliantly, is a problem the whole industry is facing.
Seekr has joined the MACH Alliance as an Agent Ecosystem Collaborator. We joined to work on the question that follows interoperability: once agents from different vendors work together, who can explain what they decided?
The MACH Alliance is a vendor-neutral nonprofit that certifies composable enterprise technology. It’s home to more than 116 member companies, including Vercel, Deloitte Digital, and Netlify. In late 2025, the Alliance launched the Agent Ecosystem, an initiative now backed by more than 70 supporters, to build an environment where specialized AI agents from different vendors work together through open standards instead of inside a single platform.
The Agent Ecosystem is a collective of industry experts co-creating reference architectures, operating patterns, and enterprise-scale proofs. Seekr joined because beyond basic interoperability , when agents work across vendor boundaries, someone is still accountable for the decisions they make.
The focus on production is why we joined. The Agent Ecosystem is about AI running at scale and driving business outcomes, not pilots that never leave the sandbox. In our experience, that’s exactly where enterprise AI stalls. Trials succeed, then the move to production is blocked by the questions leadership, risk, and compliance teams ask once real decisions are on the line: where did this come from, how was it derived, and who is accountable for it? Interoperability gets agents working together across vendor boundaries. It doesn’t answer those questions, and without answers, AI stays a trial. Explainability is what clears that path, and it’s what we bring to the Ecosystem.
Explaining a decision is harder than making one
An agent chain that crosses vendor boundaries crosses definitions too. Each component carries its own idea of what counts as a record, what gets retained, and what a handoff means. Connect enough of them and the job of tracing who owns the responsibility for the decisions being made gets exponentially more difficult.
Observability is what most teams believe they need, but observability isn’t the same as explainability. Observability tells you what happened in a system, whereas explainability tells you why a decision came out the way it did, traced back to the evidence behind it. When a regulator, an auditor, or your own risk committee asks about one specific decision, “That’s what the model told me!” isn’t an acceptable answer. Explainability is what answers that question with evidence.
Seekr builds decision-ready, explainable, sovereign AI for organizations working under that kind of scrutiny, including energy, financial services, healthcare, supply chain, government, and others. Four principles govern whether an AI system’s decisions can be defended, and they’re what we bring to the Agent Ecosystem:
- Provenance comes first. Before you can explain a result, you have to know what source data drove it. This is where frontier models have real difficulty, because pointing back to source reliably is an approximation rather than a record, and you can’t hand an auditor an approximation.
- Lineage is the other half of that record. Knowing the source isn’t enough if you can’t show how the data was transformed along the way, tracked through attribution at the context, data, model, and agent level.
- Explainability is the product of the first two. Once you can show the source data and trace how it was transformed, you can show why the system produced a given result. Without provenance and lineage underneath it, you have a record of activity rather than a governance capability.
- Sovereignty is an architectural requirement, not a deployment preference. Your data has to reside and be accessed under the laws of the nation that governs it, including in cloud, on-premises, and even in air-gapped environments, with explainability intact. Residency without explainability isn’t sovereignty.
In a multi-vendor agent chain, all four must survive from one agent to the next and cover all AI-driven outputs and decisions. A policy that applies inside one vendor’s agent and stops at its boundary isn’t sufficient because the decision doesn’t stop there either.
Where we fit in the Collaborator group
The Alliance created the Collaborator category to bring AI-native companies into the Agent Ecosystem MACH groups Collaborators into four areas: agent frameworks and orchestration; observability, evaluation, and governance; workflow automation and integration; and data and knowledge infrastructure.
We’ve joined on the governance and evaluation side of that architecture, which is our area of expertise.
We’ll be working alongside fellow Collaborators Siteimprove, Zapier, Ocula Technologies, Everworker, Mastra AI, ReFiBuy AI, viax, Spur, and Sector8.
What we’ve committed to
Over the next 12 months, we’ll serve on an Agent Ecosystem working group and contribute to composable architecture principles and interoperability standards as they extend into agentic systems. We’ll collaborate with other participants on reference architectures, operating patterns, and enterprise-scale implementations. And we’ll share what we learn from production deployments in regulated and government environments with the MACH community.
Explainable AI is critical in environments where a decision has to be defended to someone with the authority to reverse it. Sharing our lessons learned creates more value when the whole industry can use and improve them than when they only sit in one vendor’s documentation.
What’s next
We’re joining the Agent Ecosystem working groups, where the reference architectures and interoperability standards for agentic systems are actually being drafted. Our contribution will center on governance and trusted AI: making sure provenance, lineage, explainability, and sovereignty are treated as requirements that carry across every agent handoff rather than features that end at a vendor boundary.
We’ll also be working with the Alliance and fellow members on content that advances the MACH message on interoperability, including webinars, white papers, and case studies that apply MACH principles to the accountability questions regulated organizations face. Our subject matter experts have spent years deploying explainable AI in commercial, government, and other high-scrutiny environments, and that’s the perspective we’ll bring.
And we’re looking for partners. MACH ISVs and SIs that share our focus on explainable, governable AI in regulated environments are the ones we want to work alongside, whether that’s a joint reference implementation, a shared customer deployment, or a co-authored point of view. If that describes you, we’d like to talk.
Read the Seekr-Mach Alliance press release here.
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