AI agents are doing more on their own these days, and that freedom comes with a catch: someone has to make sure they’re acting responsibly. If there’s no clear way to define what an agent can do or to check what it actually does, things can go wrong fast from mistakes to misuse or even breaches of trust.
Giving agents built-in responsibilities changes the game. With verifiable credentials, identification, permissions, and accreditations, organisations can set clear rules for each agent and monitor that those rules are being followed. It’s not simply about control; it’s about making their actions transparent and verifiable.
At cheqd, we’ve built the foundation that makes this possible. Our technology lets organisations assign identities, permissions, and accreditations to agents and verify them through trusted registries. That foundation means AI agents can operate in a way that organisations and their partners can trust, with accountability baked in from the ground up.
The Building Blocks of Responsible AI Agents
Building accountable AI agents requires clear definitions of what an agent can do and having a way to verify that they’re doing it right. There are a few components that enable this level of responsibility:
First comes agent identification. This is the anchor point for everything that follows. It establishes a unique, verifiable identity for each agent. No duplicates, no fakes, no anonymous actors slipping through the system. With a secure identity in place, every action the agent takes can be linked back to a real, traceable entity.
Question it answers: Who is this agent?
Example: This is SupportBot #92 created by Company X.
Then comes agent accreditations. Think of these as the agent’s credentials or badge of authority. They show that the agent is qualified to perform certain tasks. And in some cases, even issue credentials itself. Giving an agent the right accreditation is a way to make sure only trusted, capable agents are handling important functions, which helps prevent errors or misuse.
Question it answers: What is this agent trusted or qualified to do?
Example: This agent is accredited as an identity verifier.
Next come permissions. These are essentially the guardrails that keep an agent on track. They define what an agent is allowed and not allowed to do. With clear permissions in place, agents can operate safely within their boundaries, while organisations maintain oversight without needing to monitor every move.
Question it answers: What is this agent allowed to do right now in this context/system?
Example: This agent can verify users but cannot delete accounts.
All of these are anchored by trust registries. Think of them as an official record where all identifications, accreditations, and permissions are stored and verified. Whenever there’s a question about whether an agent is authorised to take an action, the trust registry provides the answer in real time. It keeps the entire system accountable and verifiable.
Put together, these building blocks create AI agents that are beyond smart, they’re responsible, accountable, and trustworthy. Organisations can let agents operate in complex environments while knowing there’s a system in place to verify every step they take.
Current Capabilities of cheqd
At cheqd, we provide the foundation infrastructure for accountable AI agents, enabling organisations to define and verify what their agents are allowed to do. Everything begins with identification. Before permissions or accreditations can be assigned, an AI agent must be uniquely identified so its actions can be linked back to a verifiable entity. Using Decentralized Identifiers (DIDs), cheqd ensures every agent has a secure, tamper-proof identity that can be proved on demand. This prevents anonymous or spoofed agents from operating in a system and establishes a root of trust for everything that follows.
Organisations can then assign accreditations to agents using Verifiable Credentials, formally recognising their capabilities and authority. These cryptographically signed credentials allow agents to prove they are trusted to perform specific tasks or issue credentials themselves. Accreditations can be chained and can be traversed back to a Root of Trust, such as a governing authority for a particular ecosystem. This creates a verifiable baseline of trust.
In addition, cheqd enables organisations to assign permissions to agents using the same verifiable credential model, specifying the scope of actions they are allowed to perform and under what conditions. Through cheqd’s Model Context Protocol (MCP), these accreditations and permissions are queryable. An agent can be challenged with a “/whois” query in real time: “Identify yourself and prove what you’re allowed to do.” The agent must respond with cryptographic proof tied to its DID-based identity and permission credentials, giving organisations a way to validate authorisation before execution rather than analysing actions after the damage is done.
Our trust registry allows organisations to query and verify agent identities, accreditations and permissions, ensuring that any claim made by an agent can be checked against an authoritative record in real time. The trust registry acts as a lookup and verification layer, ensuring that issued credentials are valid, not revoked, and assigned by a trusted issuer.
By focusing on this foundational layer, cheqd lays the groundwork for organisations and partners to build responsible AI systems. While we provide the core infrastructure for accountability, partners can expand on this foundation to implement guardrails, logging, agent-to-agent communication, behaviour monitoring, and regulatory reporting, creating fully auditable and responsible AI ecosystems.
What Our Partners Could Enable with cheqd
While cheqd provides the foundation for embedding trust and accountability into AI agents, the broader ecosystem will evolve as additional capabilities are built on top. Our partners are exploring and could enable advanced responsibility and control mechanisms by leveraging the permissions and accreditations anchored on cheqd’s trust infrastructure. These capabilities are not yet supported today, but cheqd enables the pathway for them to emerge.
By taking the verified permissions attached to an AI agent, partners could build guardrails around agent behaviour, ensuring that an agent operates strictly within the scope of what it has been authorised to do. This would move accountability from theory to enforcement. In addition, partners could enable agent-to-agent communication, where agents not only interact but also verify one another’s permissions before collaborating. This would introduce trusted delegation between autonomous systems.
Another important future capability is logging and auditing of agent actions. Partners could build services that capture a transparent history of what an agent has done, providing organisations with traceability for compliance, troubleshooting, and accountability. Alongside this, partners could compare an agent’s actions against its assigned permissions, detecting unauthorised behaviour and preventing misuse or escalation risks.
cheqd’s infrastructure sets the groundwork for agents to prove who they are and operate with clear rules and accountability. While don’t support these capabilities just yet, our ecosystem is moving in that direction, and we’re actively working with partners to bring this vision to life.
Building Fully Responsible AI Ecosystems
Trust and accountability need to be built into the very DNA of autonomous systems to make agents reliable and responsible. cheqd provides the foundational infrastructure to make this possible, allowing organisations to assign verifiable identities, permissions, and accreditations, creating a trusted baseline for how agents behave.
The true potential emerges when this foundation is combined with partner-driven innovations. cheqd and its partners are collaborating closely to shape fully auditable, responsible AI ecosystems, where every agent and action can be held accountable.
Reach out to cheqd at [email protected] to enable trust in your AI agents.