Article

Agentic Data Infrastructure Must Be Open

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January 21, 2026

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3 min

Summarize this article with:

When teams talk to me about deploying AI agents in production, the conversation rarely stays on capabilities for long. It shifts quickly to control.

In regulated environments like banking, control, data ownership, and the ability to enforce security and compliance matter more than agent capabilities. When those things are built in from the start, trust follows naturally.

Teams appreciate the convenience of SaaS tools, but when the stakes are high, control always wins out. That’s where open source has an advantage that closed systems can’t match.

The Trust Requirement Nobody Talks About

Agents aren't like other software. They're not querying dashboards or generating reports for humans to review. Instead, they access enterprise data, customer communications, transaction histories, and product usage records. And they're making decisions based on that data.

Every action needs to stay governed and reversible.

An agent handling support tickets sees customer complaints, payment history, and internal notes. In sales, it accesses contract terms, pricing negotiations, and strategic account information. In HR, it touches compensation data, performance reviews, and termination records.

This level of access requires systems designed for control. Teams need to see what runs, define where data lives, and audit every action. That means deployment models that respect data sovereignty, clear permission boundaries, and audit logs that hold up under scrutiny.

Innovation Moves Too Fast for Closed Systems

Innovation moves too fast for any single company to figure out the best patterns alone. There's so much discovery and learning happening. That’s why many companies, such as Langchain, focus on getting their tools into the hands of as many teams as possible and pay close attention to how those teams use them.

Nobody has this fully solved yet. When the first LLMs appeared, people built basic "Hello, World!" demos. Then teams began to discover what these models could do and how far they could push them. That kind of progress only happens when teams open up the technology.

Open source makes this possible.

When hundreds of teams build on the same infrastructure, learning happens faster. They surface edge cases, fix real problems, and develop new patterns through production use. Technology evolves at the speed of the ecosystem.

Open Standards Win Because Adoption Is Everything

A little over one year ago, Anthropic released MCP as an open standard.

Before that, vendors built custom integrations for every agent, and each platform had its own connection patterns. Every team solved authentication, rate limits, and schema variations independently.

Now there's one standard protocol that works across models, platforms, and tools. The Model Context Protocol is powering agentic capabilities, and anyone can build on it.

Rapid adoption is the key to innovation. Within months, the community rallied around MCP.

The data ecosystem thrived on open standards and shared connectors. The modern data stack emerged because teams could compose Kafka, Airflow, dbt, and dozens of other tools into architectures that fit their actual needs.

Agentic systems will only thrive in the same way.

Sovereignty Isn't Optional Anymore

Sovereignty matters more than convenience across industries.

Organizations must keep data within geographic boundaries and maintain provable audit trails. In an audit, they need to show exactly where data lives, who accessed it, and what actions were taken.

Open means sovereign infrastructure that runs in your environment, on your terms.

When agents access your most sensitive data and take actions on your behalf, open-source infrastructure lets you see exactly what's happening, audit every access, and maintain full control over what agents can do.

Security Through Transparency

Open standards became the foundation of modern security. Protocols like HTTPS and SSH rely on cryptographic standards such as TLS. Anyone can read the specifications or audit the implementations. And that transparency is exactly why we trust them.

Open source enables rigor. When the code is public, you can't hide vulnerabilities behind obscurity. You have to actually solve security problems because thousands of eyes are reviewing your work.

The same principle applies to agentic data infrastructure.

When agents are handling HR data, financial records, and customer information, you need complete visibility. Every action has to be observable and traceable to the data that informed it. Access must be governed by clear policies and enforced at the source, not after data has been copied.

Closed systems ask you to trust them. Open systems let you verify.

The Three Pillars of Open Agentic Infrastructure

An open agentic data infrastructure should be organized around three pillars:

1. Shared Connectors

Open, composable connectors that the ecosystem can improve together.

When a vendor changes their API, the fix gets implemented once and shared across the ecosystem. Community-driven standards where integration patterns emerge from production usage, not vendor preference. The best approach wins through adoption, not marketing.

2. Transparent Governance

Auditable pipelines where teams can trace, explain, and verify every data access and action.

When an agent makes a decision, you can see exactly what information it used and why. If an agent approves a refund, you see which transaction history it checked, which customer tier it verified, and which policy rule it applied.

Agents discover entities across systems, fetch fresh data with proper access controls, and write changes back with full traceability. Every decision can be audited back to the data that informed it, and policies govern what agents can access and modify.

3. Sovereign Deployment

Infrastructure exists in environments controlled by the organization operating it. Teams choose where deployments run, how data residency works, and where execution boundaries sit.

This model matters most for enterprises handling regulated data, where on-premises or hybrid deployments keep sensitive information inside the organization’s environment while remaining transparent and auditable.

The Commitment Required

Keeping agentic data infrastructure open requires real commitment. It's not just about releasing code on GitHub and calling it a day. We need:

Open connector standards: Teams can rely on them, improve them together, and avoid getting locked into vendor-specific integrations.
Enterprise-grade governance: Clear visibility and control make security and trust work together instead of fighting each other.
Working openly across the ecosystem: Partnering with open-source AI frameworks, data platforms, and orchestration tools turns interoperability into shared infrastructure rather than private silos.

The future of intelligence is too big for one single vendor, model, or framework to own it. It depends on a community willing to build in the open, hold itself to higher standards, and treat shared infrastructure as a shared responsibility.

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