A Category That Did Not Exist a Year Ago
For most of the AI governance era, "proof" meant logs — mutable, reconstructable-after-the-fact, and useless the moment they were contested. In 2026 that changed. A distinct category formed, fast: cryptographic decision proof for AI agents.
The thesis behind it is now stated almost identically by a dozen vendors: the problem isn't logging — it's proof. Logs can be edited, lost, or backdated. When an agent approves a payment no human reviewed, routes data that causes downstream harm, or makes a decision later disputed in an audit, an editable log is not evidence. What regulators, auditors, and counterparties actually want is a tamper-evident record, sealed at the moment of the decision, independently verifiable.
This piece maps that landscape as it actually looks in mid-2026 — including the players a generic search misses — and shows where an embedded, vertical Decision Proof Unit (DPU) sits relative to the new wave of neutral, horizontal proof APIs.
Why Now: The Regulatory and Market Forcing Functions
Three forces created the category almost simultaneously:
- EU AI Act enforcement. High-risk obligations — automatic logging (Art. 12) and effective human oversight (Art. 14) — are in force, with GPAI enforcement intensifying from August 2, 2026.
- US National AI Legislative Framework (unveiled March 20, 2026), calling for consistent national standards against AI-enabled fraud and for responsible deployment.
- Agent proliferation. Gartner projects task-specific agents embedded in ~40% of enterprise software by end of 2026 (up from <5% in 2025). The OWASP Agentic Security Top 10 launched, reportedly finding ~36% of agent skills have security flaws; UiPath completed the first AIUC-1 agent certification (Apr 2, 2026).
Capital followed: agent-runtime and identity startups raised quickly (e.g., Onyx, reported ~$40M for agent runtime security; Keycard, ~$38M for agent identity), while governance-platform funding concentrated at the top of the market. Procurement is the real driver — as one vendor puts it, enterprise buyers "don't block deals because your agent doesn't work; they block them because they can't verify what it did."
The Map: Six Layers, One Crowded New Frontier
"AI governance" spans distinct layers that solve different problems. Conflating them is the most common buyer mistake.
| Layer | What it does | Representative players |
|---|---|---|
| Model governance / GRC | Inventory, bias/fairness, compliance dashboards | Credo AI, Holistic AI, IBM watsonx.governance |
| Observability / evals | Trace runs, evaluate outputs, debug | Arize, Arthur, Galileo, LangSmith, Fiddler |
| Agent control plane / policy | Pre-execution policy, scoped tool calls | Cordum, Microsoft Agent Governance Toolkit, Guild.ai, systemprompt.io, Rubrik Agent Govern |
| Agent identity / security | Identity, zero standing privilege, runtime security | CyberArk Secure AI Agents (Palo Alto Networks), Keycard, Onyx, Microsoft Authorization Fabric |
| Trust / attestation platforms | Cryptographic attestation + HITL + audit trails | OpenBox AI, Foundational |
| Decision proof (the new frontier) | Tamper-evident, hash-chained receipts of what was decided/reviewed | EverMint, ProofRelay, AgentMint, Attested Intelligence, Ordit, PiQrypt — and Cronozen DPU |
The first five layers are crowded but understood. The sixth — decision proof — is where the action is, and where a generic market survey under-reports because most of these players launched in the last few months.
Inside the Decision-Proof Category
These vendors converge on a near-identical mechanism — hash a decision payload, timestamp it, chain it to the prior record, make it independently verifiable — but differ in scope and surface.
| Vendor | One-line positioning | Proof mechanism | Notable angle |
|---|---|---|---|
| EverMint | "Immutable records for AI agents" | Hash + cryptographic timestamp, chained; "the chain cannot be back-dated" | Model-agnostic; "give your agents an alibi"; anchors the moment a human approved |
| ProofRelay | "Decision receipts for autonomous agents" | HMAC-SHA-256 seal of inputs + ruleset + reasoning as one atomic unit; public verify, no account | Three-timestamp lifecycle (captured / decided / sealed); seals before execution |
| AgentMint | "Runtime enforcement for tool calls" | Ed25519 signature + SHA-256 hash chain per action; VERIFY.sh, no vendor software |
MIT-licensed; "evidence comes from math, not us"; buyer-runs-one-script security review |
| Attested Intelligence (AGA) | "Prove every decision cryptographically" | Sealed policy at build time, signed receipts, Merkle trees, post-quantum (ML-DSA-65) | Offline / air-gapped verification; MCP governance proxy on npm; agent holds no keys |
| Ordit | "Verifiable decision records" | Server-side cryptographic receipts + hash chains | Works with any automated decision system (ML, rules, heuristics) — AI not required |
| PiQrypt | "Verifiable continuity layer" | Ed25519 / Dilithium3, hash-chained journal + risk scoring (TrustGate) | HITL queue with clearance levels; "proof of disobedience" if an agent ignores a block; EU AI Act Art. 14 |
A few patterns matter for buyers:
- Most are neutral, horizontal "proof APIs." You call them per action (
POST /decision), they return a signed receipt. They are deliberately not the system making the decision, not observability, not a substitute for human oversight. That neutrality is their pitch — and their boundary. - Public / offline verification is becoming table stakes. ProofRelay, AgentMint, Attested Intelligence, and PiQrypt all let a third party verify without trusting (or even accessing) the vendor.
- Human-in-the-loop is acknowledged but usually shallow. Several "anchor the moment a human approved." Fewer capture the quality of that review — how long, what changed, why it was overridden, whether the reviewer was qualified.
The takeaway: the "proof gap" is no longer empty — it is filling fast. The honest 2026 question is not "does decision proof exist?" but "what kind of proof do you need, and where should it live?"
Two Architectures: Neutral Proof API vs. Embedded Vertical Proof
The category splits on a single axis: where does the proof layer live?
Neutral proof API (most of the above). A standalone service you integrate into your own stack. Strengths: model-agnostic, fast to adopt, independently verifiable, no lock-in. Trade-off: it proves what was submitted to it. It does not know your domain, your approval policy, or whether the human reviewer was meaningfully engaged — because it sits outside your operational workflow.
Embedded vertical proof (Cronozen DPU). Proof generated as a byproduct of the operational platform itself. Strengths: it captures the full decision unit — AI decision + human review + executed action — inside the real workflow, with domain context. Trade-off: it is not a neutral, drop-in API for arbitrary stacks; it is strongest when you operate inside the platform.
Neither is "better" in the abstract. They answer different questions:
| Question | Neutral proof API | Embedded vertical DPU |
|---|---|---|
| "Was this record altered?" | ✅ Yes | ✅ Yes |
| "Did a human review it?" | Often: anchors an approval event | ✅ Captures review duration, modifications, override reason, reviewer role |
| "What domain policy applied?" | You must supply it | ✅ Known from the vertical workflow |
| "Is it embedded in daily operations?" | No — you wire it in | ✅ Yes — proof is automatic |
| "Independently verifiable by a third party?" | ✅ Usually a core feature | ✅ Hash-chain verifiable |
Where Cronozen DPU Fits
Cronozen's thesis is "infrastructure for keeping AI decisions under human control and provable," implemented as the Decision Proof Unit (DPU) — and the 2026 landscape sharpens, rather than threatens, that position.
- Same cryptographic core as the new category. DPU writes the decision, the human review, and the executed action into a SHA-256 hash chain — tamper-evident, exactly like EverMint / ProofRelay / Ordit.
- Deeper human-oversight capture. Where most neutral APIs anchor an approval, DPU records review quality — duration, modifications, rejection reasoning, reviewer qualification — meeting the bar of "effective oversight" (EU AI Act Art. 14), not just "someone clicked approve."
- Embedded in vertical operations. DPU is generated inside real workflows (settlement confirmation, voucher claims, document submission) across 16+ domain helpers — so proof is a byproduct of daily work, not a separate integration project.
- Complements, not competes with, Layers 1–5. Pair model governance (Credo AI), observability (Arize/Fiddler), and agent control planes (Cordum/MS AGT) with DPU for the decision-level, human-reviewed proof those layers rarely capture.
In landscape terms: the neutral proof APIs are racing to give any stack a verifiable receipt. DPU's bet is that for regulated verticals — healthcare, welfare, education, public services — proof has to live inside the operational system and capture the human's role, because that is what an auditor in those domains actually asks for.
How to Choose
- Decide what you must prove. "What the agent did" → a neutral proof API (EverMint, ProofRelay, AgentMint, Ordit) is fast and sufficient. "What was decided and how a qualified human reviewed it, inside a regulated workflow" → an embedded vertical proof layer.
- Demand independent verification. Ask any vendor: can a third party verify one specific decision, offline, without your software? The strong players say yes.
- Don't confuse layers. Observability debugs; control planes enforce; identity scopes; proof attests. You will likely combine several — but only the proof layer survives a dispute.
- Watch consolidation. The category formed between roughly February and May 2026; expect rapid feature convergence and acquisitions (CyberArk's agent line already sits inside Palo Alto Networks).
The Bottom Line
A year ago, "prove what your AI decided" had no good answer. In 2026 it has many — EverMint, ProofRelay, AgentMint, Attested Intelligence, Ordit, PiQrypt, and Cronozen DPU among them. The decision-proof category is real, crowded, and validating the premise that logs are not evidence.
The differentiator is no longer whether you can mint a tamper-evident receipt. It is how much of the decision you capture — and whether the human's role is in the record. For regulated operations, that is the line that matters:
When your AI decision is challenged, can you prove what was decided, who reviewed it, how well they reviewed it, and that the record was never altered?
References
- EverMint (evermint.app); ProofRelay (proofrelay.app); AgentMint (agentmint.run); Attested Intelligence / AGA (attestedintelligence.com); Ordit (ordit-ai.com); PiQrypt (github.com/PiQrypt/piqrypt, Feb 2026)
- OpenBox AI launch + $5M seed (PR Newswire, Mar 31, 2026); Foundational + $8M seed (BusinessWire, May 2026)
- Cordum agent-control-plane comparison (cordum.io, Mar 2026); systemprompt.io "AI Governance Tools Compared" (Apr 2026)
- Microsoft Agent Governance Toolkit (MIT, Apr 2, 2026); Galileo Agent Control (Apache, Mar 2026); CyberArk Secure AI Agents (Palo Alto Networks, acquisition closed Feb 11, 2026)
- Funding signals: Onyx (
$40M, agent runtime security); Keycard ($38M, agent identity) - Regulatory: EU AI Act Art. 12 / Art. 14, enforcement from Aug 2, 2026; US National AI Legislative Framework (Mar 20, 2026); OWASP Agentic Security Top 10; AIUC-1 (UiPath first certification, Apr 2, 2026); ISO/IEC 42001; SOC 2 for AI
Related reading — "Best AI Governance Platforms 2026: Where DPU Fits in the Stack," "AI Audit Trail vs Decision Proof Unit," and "Agentic AI Governance in 2026: Why Human-on-the-Loop Needs a Proof Layer" in the Cronozen AI Compliance category.