As artificial intelligence agents become more capable and more autonomous, a foundational question is emerging in both technical and ethical terms:
What does it mean for an agent to be trustworthy?
We are rapidly entering a world where autonomous agents not only make recommendations, but also execute code, provision infrastructure, train models, sign software packages, publish content, and negotiate with other agents on our behalf. In this world, the traditional boundaries of identity, authorization, and accountability start to blur.
From Human Trust to Agentic Trust
Human-centered trust relies on relationships, credentials, and context. We trust a colleague because we know their track record. We trust a news source because we’ve verified its consistency. We trust a certification because it was issued by a recognized authority.
But autonomous systems don’t have intuition or memory in the way we do. They don’t just need to be authenticated—they need to be verifiable. And for this, we need agentic trust.
Agentic trust is the ability to establish and reason about the trustworthiness of autonomous agents—not just based on who they claim to be, but what they’ve done, how they were created, who authorized them, and what policies govern their actions.
Why Agentic Trust Matters
Agentic trust isn’t science fiction. It’s becoming a practical concern across multiple domains:
In DevOps, where agents commit code, trigger builds, and sign artifacts.
In content platforms, where agents generate media, publish posts, and make decisions.
In federated learning, where models are trained collaboratively across untrusted parties.
In security, where agents monitor, patch, and respond to threats in real time.
Each of these workflows introduces risk if we can’t trace actions back to verifiable origins and policies.
The New Trust Stack
The challenge isn’t just making agents do the right thing—it’s enabling systems to verify that agents are doing the right thing, according to policies we understand and control.
The identity and access management (IAM) market is filled with access-control solutions—role-based (RBAC), attribute-based (ABAC), and now reasoning-based (ReBAC). These systems answer who gets access to what, and increasingly why.
But in a world of autonomous agents and generated actions, access alone isn’t enough. What’s missing is trust in the action itself—before access is even requested.
That’s where we come in: Agentic Trust Infrastructure, built on verifiable metadata, signed provenance, and graph-based reasoning.
That requires a new kind of trust stack, one that combines elements from several evolving domains:
Provenance frameworks like in-toto and SLSA to describe how artifacts or decisions were produced.
Cryptographic attestations and signatures to establish integrity and authorship.
Decentralized identity systems (DIDs) to represent agents and services without relying on centralized authorities.
Verifiable Credentials (VCs) to assert claims about an agent’s capabilities, training, affiliations, or certifications.
Policy engines like OPA or Cedar to make trust decisions based on verifiable context.
Immutable storage and addressing (e.g., IPFS or content hashes) to make these records tamper-evident and reproducible.
This is not about reinventing identity. It’s about extending trust into agent-based systems, workflows, and decisions.
Just as data loss prevention (DLP) is being reinvented with LLMs for intelligent classification, identity and access control must evolve. No human can manually define policies granular enough for the agent to agent protocol.
Trust as a Graph, Not a Gate
Perhaps most importantly, agentic trust requires thinking in terms of graphs rather than gates.
In traditional models, authorization is often a binary: “Does this user have access?” In a composable, decentralized system, the question becomes richer:
Is this artifact derived from a trusted source?
Did this agent follow an approved process?
Was this action signed by a credentialed authority?
Do all elements in the chain meet policy thresholds for integrity and attribution?
This graph-based reasoning aligns with how humans already think about trust: not as a single yes/no answer, but as a web of evidence.