Three layers unlock verifiable AI agents: ZKML, TEEs, and identity enforcement
The agentic AI market is bottlenecked by inference costs, verification, and governance. Emerging infrastructure from Mina, Terminal 3, and Strata Identity shows a layered stack that makes agent actions provable, private, and auditable for crypto use cases.
The agentic AI market is stuck in pilot purgatory. Three bottlenecks—inference costs, verification, and governance—keep most deployments from scaling past pilot [^claim_957]. For crypto-native agents that trade, lend, or govern on-chain, these aren’t just engineering problems. They’re trust problems. A trading bot that can’t prove it followed its strategy is a liability. A DAO-controlled agent that can’t be audited is a governance hole.
Three emerging infrastructure layers solve this in sequence: zero-knowledge machine learning (ZKML) for verifiable inference, trusted execution environments (TEEs) for confidential compute, and identity-enforced governance for agent-to-tool communication.
ZKML: Proving the computation, not the semantics
ZKML lets a model generate a cryptographic proof that a given output came from a specific computation on specific inputs, without revealing either the inputs or the model weights [^claim_947]. Mina’s zkML library pushes this further: anyone can generate a zero-knowledge proof from an AI inference job using private inputs, settle that proof on the Mina blockchain, and guarantee the output hasn’t been tampered with—while keeping both input data and the model private [^claim_945]. Modulus Labs benchmarked proof systems for on-chain inference on models up to 18 million parameters, showing ZK proofs can handle non-trivial ML model sizes [^claim_946]. The worldcoin-maintained ‘awesome-zkml’ index documents concrete systems like Zator, which runs verified inference of a 512-layer neural network using recursive SNARKs, and a generic SNARK for ML inference that can prove and verify that an output is the product of a given model–input pair [^claim_948].
ZKML plus on-chain provenance makes the entire chain of inference verifiable end-to-end. In a multi-agent workflow, a compromised agent is detectable because every agent’s computations and data commitments are individually provable [^claim_954]. On-chain AI computation runs via ZK rollups and ZKML: proofs of correctness are generated off-chain and verified on-chain, cutting gas costs while keeping verifiability intact [^claim_955].
TEEs: Confidential compute with cryptographic integrity
Not every agent workflow needs full ZK proofs. For private data that must stay confidential during computation, TEEs offer a different path. Terminal 3’s confidential compute network runs AI agents inside TEE nodes, providing secure storage of private data, fully secure computation across a network, and cryptographic integrity via signatures on every action and transaction—all coordinated through a confidential consortium ledger with tokens to pay for compute [^claim_950]. This is effectively a blockchain with tokens for compute, but not a fully public mainnet—a design choice that balances transparency with privacy.
EQTY Lab, Intel, and NVIDIA’s Verifiable Compute solution for Hedera uses advanced cryptography to protect and control AI data, models, and agents, offering verifiable execution and governance capabilities as autonomous AI agents become more prevalent [^claim_949].
Identity and governance: The access-control layer for agents
Proofs and TEEs verify what happened. Governance ensures only authorized agents can act. Strata Identity recommends governing the Model Context Protocol (MCP) as a first-class access channel for AI agents: every MCP request should flow through an enforcement point that validates the agent’s identity, checks authorization against policy, and produces an audit record [^claim_951]. Every AI agent should have a distinct, verifiable identity separate from its human users and collaborating agents. All agent authentication events, tool invocations, delegation handoffs, and policy decisions should be captured in an auditable format for real-time monitoring and compliance [^claim_958].
The stack in practice
These layers are already being assembled. Crypto.com’s AI Agent SDK exposes an HTTP interface that lets applications send natural-language questions and optional end goals as query parameters, enabling agents to interact programmatically with Cronos EVM and Cronos zkEVM [^claim_953]. OpenAI’s Agents SDK provides built-in tools and integrated observability to trace and inspect agent workflow execution [^claim_952]. The AWS 2025–2026 taxonomy defines AI agent frameworks as toolkits that package LLMs, tool integrations, memory, orchestration, and safety features, and recommends a tiered model routing strategy [^claim_956].
They are a stack. ZKML proves inference integrity, TEEs protect private state, and identity enforcement governs access. Protocols that integrate all three—like Mina for proofs, Terminal 3 for confidential compute, and Strata-style governance for authorization—will be the ones that scale past pilot.
Provenance ledger
14 claims web-citedEvery claim below cites a source URL, and each URL was checked for validity before publish. The excerpt shown is the researcher's own summary of the page — it is not re-derived from the source, so it is not a verified verbatim quote. Follow the link to confirm any claim against the original. Citation markers in the text jump here.
[1] Mina’s zkML library allows developers to generate a zero-knowledge proof from an AI inference job with private inputs and settle that proof directly on the Mina blockchain, guaranteeing that the output has not been tampered with while keeping both the input data and the model private. web-cited
“Mina’s zkML library enables anyone to generate a zero knowledge proof from an AI inference job using private inputs. This means you can trust the output has not been tampered with and you can keep your private data private… settling those proofs on the Mina blockchain.” [9]
This excerpt was not re-derived from the source page, and may paraphrase or condense it. Check the source before relying on it.
[2] Modulus Labs has benchmarked proof systems for on-chain inference on models up to 18 million parameters, demonstrating that zero-knowledge proof systems can handle non-trivial ML model sizes for verifiable compute on blockchains. web-cited
“Modulus Labs shows the benchmarking proof systems for on-chain inference up to 18 million parameters. Gensyn is developing sophisticated…” [6]
This excerpt was not re-derived from the source page, and may paraphrase or condense it. Check the source before relying on it.
[3] Zero-knowledge machine learning (ZKML) enables a model to generate a cryptographic proof that a given output was produced by a specific computation on specific inputs, without revealing either the inputs or the model weights, thus proving integrity of the computation but not semantic correctness. web-cited
“Zero-knowledge machine learning, ZKML, is the technical approach… the concept: a model generates a cryptographic proof that a given output was produced by a specific computation on specific inputs without revealing the inputs or the model weights themselves… Blockchain verifies the computation was performed. It can't verify that the computation was correct and meaningful in a real-world use case.”
This excerpt was not re-derived from the source page, and may paraphrase or condense it. Check the source before relying on it.
[4] The worldcoin-maintained ‘awesome-zkml’ index documents concrete systems for verifiable ML inference, including Zator, which implements verified inference of a 512-layer neural network using recursive SNARKs, and a ‘Generic SNARK for ML inference’ that can prove and verify that an output is the product of a given model–input pair. web-cited
“Zator - Verified inference of a 512-layer neural network using recursive SNARKs… Generic SNARK for ML inference: ability to easily prove and verify that an output is the product of a given model and input pair.”
This excerpt was not re-derived from the source page, and may paraphrase or condense it. Check the source before relying on it.
[5] EQTY Lab, Intel, and NVIDIA’s Verifiable Compute solution for Hedera uses advanced cryptography to protect and control AI data, models, and agents, providing verifiable execution and governance capabilities as autonomous AI agents become more prevalent. web-cited
“Today, EQTY Lab, Intel, and NVIDIA unveiled Verifiable Compute. The new, transformative solution addresses a critical need in the AI industry for trust and verification, particularly as autonomous AI agents become more prevalent… Verifiable Compute leverages advanced cryptography to protect and control AI data, models, and agents.” [3]
This excerpt was not re-derived from the source page, and may paraphrase or condense it. Check the source before relying on it.
[6] Terminal 3’s confidential compute network runs AI agents inside trusted execution environment (TEE) nodes, providing secure storage of private data, fully secure computation across a network, and cryptographic integrity via signatures on every action and transaction, all coordinated through a confidential consortium ledger with tokens to pay for compute. web-cited
“Terminal 3 network… a confidential compute infrastructure made up of trusted execution environment nodes… allows for… the world's most secure storage of private data… fully secure computation in a network… pure cryptographic integrity on every single action, every transaction, everything is cryptographically signed… a confidential consortium framework… effectively, a blockchain with tokens to pay for compute, but not fully public mainnet.” [5]
This excerpt was not re-derived from the source page, and may paraphrase or condense it. Check the source before relying on it.
[7] Strata Identity recommends governing the Model Context Protocol (MCP) as a first-class access channel for AI agents, requiring every MCP request to pass through an enforcement point that validates the agent’s identity, checks authorization against policy, and produces an audit record for observability and compliance. web-cited
“MCP is becoming the standard interface for agent-to-tool communication, and it needs to be governed with the same rigor applied to API gateways and network access controls. Every MCP request should flow through an enforcement point that validates the agent’s identity, checks authorization against policy, and produces an audit record.” [8]
This excerpt was not re-derived from the source page, and may paraphrase or condense it. Check the source before relying on it.
[8] OpenAI’s Agents SDK and Responses API provide built-in tools such as web search, file search, and computer use, along with integrated observability to trace and inspect agent workflow execution, enabling orchestration of single-agent and multi-agent workflows with cited, up-to-date web answers and priced computer-use at $3 per 1M input tokens and $12 per 1M output tokens. web-cited
“We’re launching a new set of APIs and tools… The new Responses API… Built-in tools including web search, file search, and computer use… The new Agents SDK to orchestrate single-agent and multi-agent workflows… Integrated observability tools to trace and inspect agent workflow execution… developers can now get fast, up-to-date answers with clear and relevant citations from the web… computer use tool… usage is priced at $3/1M input tokens and $12/1M output tokens.” [7]
This excerpt was not re-derived from the source page, and may paraphrase or condense it. Check the source before relying on it.
[9] Crypto.com’s AI Agent SDK exposes an HTTP interface that lets applications send natural-language questions and optional end goals as query parameters, enabling agents to interact programmatically with Cronos EVM and Cronos zkEVM infrastructure based on user intent expressed in plain language. web-cited
“Crypto.com AI Agent SDK allows developers to build apps where end users can interact with Cronos EVM and Cronos zkEVM using natural language… Perform an HTTP GET request… with the `ask` query parameter, and the optional `goal` query parameter.” [10]
This excerpt was not re-derived from the source page, and may paraphrase or condense it. Check the source before relying on it.
[10] The ‘definitive guide to ZKML’ emphasizes that ZKML plus on-chain provenance makes the entire chain of AI inference verifiable end-to-end, such that a compromised agent in a multi-agent workflow is detectable because all agents’ computations and data commitments are individually provable. web-cited
“The whole ‘chain’ of inference is verifiable end-to-end. Without this? One compromised agent poisons the entire workflow. In autonomous…”
This excerpt was not re-derived from the source page, and may paraphrase or condense it. Check the source before relying on it.
[11] Inference Labs describes on-chain AI computation as enabled by ZK rollups and ZKML, allowing blockchains to run certain AI computations where proofs of correctness are generated off-chain and verified on-chain, mitigating gas costs while retaining verifiability. web-cited
“On-chain AI computation: ZK rollups and zero-knowledge machine learning (zkML) helps allow blockchains to run certain AI computations, providing…”
This excerpt was not re-derived from the source page, and may paraphrase or condense it. Check the source before relying on it.
[12] The AWS 2025–2026 taxonomy defines AI agent frameworks as toolkits that package LLMs, tool integrations, memory, orchestration, and safety features, and recommends a tiered model routing strategy that sends simple classification or extraction tasks to cheaper small models while reserving large models for complex reasoning. web-cited
“An AI agent framework is ‘a toolkit that provides the building blocks to design, deploy, and manage AI agents,’ including LLMs, tool integrations, memory, orchestration, and safety features… Use a tiered model strategy: route simple classification or extraction tasks to smaller, cheaper models… and reserve larger models… for complex reasoning.”
This excerpt was not re-derived from the source page, and may paraphrase or condense it. Check the source before relying on it.
[13] The 0G analysis of the agentic AI market argues that scaling beyond pilot deployments is blocked by three main infrastructure bottlenecks—inference costs, verification, and governance—and positions decentralized compute and verification layers as the path forward to handle high agent transaction volumes. web-cited
“Infrastructure bottlenecks (inference costs, verification, governance) prevent most deployments from scaling past pilot. Decentralized compute and verification layers offer a path forward.” [1]
This excerpt was not re-derived from the source page, and may paraphrase or condense it. Check the source before relying on it.
[14] The Strata Identity governance guide asserts that every AI agent must have a distinct, verifiable identity separate from its human users and collaborating agents, and that all agent authentication events, tool invocations, delegation handoffs, and policy decisions should be captured in an auditable format for real-time monitoring and compliance. web-cited
“Every AI agent should have a distinct, verifiable identity… Governance without visibility is unenforceable. Every agent authentication event, tool invocation, delegation handoff, and policy decision needs to be captured in a format that supports real-time monitoring and compliance audits.” [8]
This excerpt was not re-derived from the source page, and may paraphrase or condense it. Check the source before relying on it.
Sources
- https://minaprotocol.com/blog/minas-zkml-library-developer-guide
- https://kudelskisecurity.com/modern-ciso-blog/zkml-verifiable-machine-learning-using-zero-knowledge-proof
- https://www.youtube.com/watch?v=vONZg4p2msE
- https://github.com/worldcoin/awesome-zkml
- https://www.eqtylab.io/blog/verifiable-compute-and-hedera
- https://www.youtube.com/watch?v=PEkeRSuypIU
- https://www.strata.io/blog/agentic-identity/agentic-ai-governance-how-to-approach-it/
- https://openai.com/index/new-tools-for-building-agents/
- https://ai-agent-sdk-docs.crypto.com
- https://blog.icme.io/the-definitive-guide-to-zkml-2025/
- https://x.com/inference_labs/article/1986116116104814686
- https://aws.amazon.com/marketplace/build-learn/ai-agent-learning-series/agent-frameworks-building-blocks
- https://0g.ai/blog/agentic-ai-market-infra-2026