When Agents Get Commoditized

The Cambrian Explosion of AI Agents

We are witnessing something unprecedented. In the last twelve months, the number of commercially available AI agents has exploded from a handful of research prototypes to hundreds of production-grade tools spanning every business function imaginable. Customer support agents, CRM agents, GTM agents, coding assistants, legal review bots, procurement copilots, security scanners. The list grows daily.

This is not just another wave of SaaS adoption. It is a fundamental shift in how work gets done. Unlike traditional software that waits for human input, AI agents act autonomously. They read your data, make decisions, call external APIs, and interact with other systems on your behalf. They are powerful. They are proliferating. And they are creating a risk management crisis that most organizations have not yet recognized.

Every Agent Is a Vendor

Here is the uncomfortable truth that security teams need to internalize: every AI agent your organization adopts is a third-party vendor relationship.

It does not matter that the agent feels like a feature or a plugin. Behind every agent sits a company, a legal entity, a data processing agreement (or the absence of one), a security posture, and a set of privacy practices. When your marketing team deploys an AI agent to manage outbound campaigns, they have just onboarded a vendor. When your engineering team integrates a coding copilot, that is a vendor relationship with access to your source code.

The difference? These vendor relationships are forming at 10x the speed of traditional SaaS adoption, with a fraction of the oversight.

Commoditization Makes the Problem Worse

As AI agents commoditize, the problem intensifies. When there are ten customer support agents to choose from, a thoughtful review is feasible. When there are two hundred, each claiming similar capabilities with subtly different data practices, the traditional approach breaks down entirely.

Commoditization also drives a race to the bottom on differentiation. In that race, security and privacy practices are often the first things to be cut. Smaller agent startups may lack the resources for SOC 2 certification, may not have clear data retention policies, and may be training their models on customer data without explicit consent. The agents that look identical on the surface may have vastly different risk profiles underneath.

The Differentiation Illusion

From a feature perspective, many agents in the same category are nearly indistinguishable. They use similar foundation models, offer similar integrations, and promise similar outcomes. The real differentiation, the part that matters to your CISO, lives in the details that are hardest to evaluate:

• Data handling: Does the agent store conversation data? For how long? Is it used for model training?
• Subprocessor chain: Which foundation model providers does the agent rely on? What are their data practices?
• Access scope: What permissions does the agent request? Are they appropriately scoped?
• Incident response: What happens when something goes wrong? Is there a breach notification commitment?
• Business continuity: What happens to your data if the startup shuts down next quarter?

TPRM Is the Bottleneck

Traditional third-party risk management was built for a world where organizations onboarded a few dozen vendors per year. The typical TPRM process, involving security questionnaires, document review, and risk committee approvals, takes four to eight weeks per vendor. That was acceptable when the pace of adoption was measured in quarters.

In the agentic AI era, teams are evaluating and adopting new agents weekly. The math simply does not work.

The result is predictable: either the security team becomes the bottleneck that kills agility, or teams bypass the process entirely and adopt agents without review. Neither outcome is acceptable. The first stifles innovation. The second creates unacceptable risk.

"The speed of TPRM is now the limiting factor for organizational agility. If you cannot assess a vendor in minutes, you cannot keep pace with how fast your teams are adopting AI agents." – Security Leader

Agent-to-Agent Risk Verification: The Solution

What if risk assessment itself could be automated? What if, instead of human analysts spending weeks filling out spreadsheets, AI agents could verify the trustworthiness of other AI agents in real time?

This is exactly what RRR has built.

The RRR MCP Server

RRR exposes vendor risk data via a Model Context Protocol (MCP) server. MCP is an open standard that lets AI agents query external data sources in a structured way. Any MCP-compatible agent can connect to RRR and immediately access:

• Vendor lookup: Instant risk scores for any vendor domain
• Risk analysis: Detailed security, privacy, and compliance assessment
• Vendor comparison: Side-by-side risk comparison of competing agents
• Trust verification: Cryptographically signed attestations of a vendor's risk posture
• Certification checks: Gap analysis against your organization's compliance requirements

This means any AI agent in your ecosystem, your procurement copilot, your IT automation platform, your security orchestration tool, can query RRR before engaging with a new vendor or agent. The risk check happens programmatically, in seconds, without a human in the loop.

The RRR Guardian Agent on Moltbook

RRR has already deployed the RRR Guardian Agent on Moltbook, a decentralized agent interaction platform. Any AI agent operating on Moltbook can now query the RRR Guardian Agent to verify the risk posture of another agent before initiating a transaction or data exchange.

Think of it as a credit check for AI agents. Before Agent A shares sensitive data with Agent B, it asks the RRR Guardian: "Is Agent B trustworthy? What is the risk profile of the company behind it? Do they have adequate security controls?"

The response comes in milliseconds, with a structured risk assessment, trust score, and cryptographic attestation.

From Weeks to Seconds

The shift from manual TPRM to agent-to-agent verification is not incremental. It is transformational. Consider the workflow:

1. Traditional approach: Employee discovers agent → submits request → security team reviews (4-8 weeks) → committee approves → agent deployed. Total time: 6-10 weeks.
2. Agent-to-agent approach: Employee discovers agent → procurement copilot queries RRR via MCP → risk assessment returned in 60 seconds → automated policy check against organizational rules → agent deployed or flagged for human review. Total time: minutes.

The critical insight is that automation does not eliminate human judgment. It reserves human attention for the cases that genuinely require it. Low-risk, well-certified agents with transparent practices get fast-tracked. High-risk agents with gaps in security posture get escalated to human reviewers with a pre-populated risk report.

Building the Trust Layer for the Agentic Economy

The bigger vision goes beyond individual risk assessments. As AI agents increasingly interact with each other, transacting data, delegating tasks, and making decisions, the agentic economy needs a shared trust infrastructure.

Every agent transaction should include a trust check. Every data exchange should be backed by a verified risk attestation. Every agent-to-agent interaction should carry a cryptographic proof that both parties have been assessed and meet minimum security thresholds.

RRR is building that infrastructure. The MCP server, the Guardian Agent, the trust attestation protocol: these are the foundational components of a trust layer that scales with the agentic economy rather than against it.

What This Means for Your Organization

If your organization is adopting AI agents (and it is, whether IT knows about it or not), you need to answer three questions:

1. Visibility: Do you know which AI agents your teams are using? RRR's Shadow IT Discovery and Browser Extension can tell you.
2. Assessment: Can you evaluate the risk of a new agent in minutes, not months? RRR's AI Risk Assessment does this in 60 seconds.
3. Governance: Can your AI tools verify other AI tools automatically? RRR's MCP server and Guardian Agent enable exactly this.

The Cambrian explosion of AI agents is not slowing down. The organizations that thrive will not be those that block adoption. They will be those that build trust infrastructure that moves at the speed of innovation.