Agentic OS, Verifiable Evidence, and On‑Chain Verdicts

Traditional dispute resolution mechanisms assume that humans can reconstruct events ex post from logs and testimony. Agentic OS layers—operating‑system level autonomous agents orchestrating AI‑driven workflows—violate that assumption. They execute continuously, mutate state in the background, and frequently leave only high‑level natural‑language traces. When a dispute or regulatory investigation arrives months later, the question is no longer “what did the user do?” but “what did the agent decide, based on which inputs, at which time—and can we prove it?”

This article examines that failure mode and outlines how Verdikta’s decentralized, multi‑model consensus—commit–reveal, attested evidence, and on‑chain verdicts on Base L2—provides a technically rigorous alternative for agentic OS deployments. It builds on ideas discussed in a prior technical brief on agentic OS risks and decentralized verdicts.

Agentic OS as an Automation Plane: Threat Model and Attack Surface

From a protocol perspective, an agentic OS is not a chat window embedded in the desktop. It is an OS‑level automation plane where autonomous agents can:

• Orchestrate workflows in the background without per‑action user prompts.
• Invoke privileged system calls for file I/O, process and service control, registry and network configuration.
• Continuously capture local data: documents, screenshots, browser profiles, telemetry, clipboard contents.

In practice, the agentic OS intermediates between user intent, local system state, and external AI models. That combination alters the attack surface for AI‑driven workflows in three ways that matter directly for later dispute resolution.

First, data exfiltration vectors multiply. A background agent with filesystem and clipboard access can scan an entire home directory, read draft legal documents, inspect browser cookies, and stream either plaintext or embeddings to a centralized model endpoint. Even if a vendor promises not to persist data, nothing in the mechanism prevents transient misuse, model training leakage, or endpoint compromise.

Second, provenance becomes opaque. Many deployments log high‑level claims—“classified invoice.pdf as fraudulent”—without cryptographic binding to the underlying bytes, prompts, model version, or active policy set. Those claims become de‑facto ground truth until challenged, at which point there is no verifiable link back to the exact inputs and configuration the agent actually saw.

Third, centralizing orchestration in a single coordinator or policy engine creates a structural single point of failure. A misconfigured auto‑policy can silently delete an evidence directory labeled as “malware,” including the only copy of a disputed contract. A persistent “copilot” agent can emit unlogged intermediate reasoning while only the final email text reaches the archive. A “smart DLP” module can misclassify a whistleblower email as benign and forward it prematurely, with no signed trace explaining the evaluation.

A realistic adversary model for an agentic OS must therefore include:

• Insider misuse. Administrators and engineers can change policies, reduce logging verbosity, inject prompts to fabricate log entries, or mark inconvenient artifacts for auto‑deletion.
• Model drift and misalignment. Centrally updated models change behavior over time without cryptographically versioned change‑control. The same prompt, months apart, yields different outcomes, and there is no signed mapping from decision to model weights or configuration.
• Supply‑chain compromise. Attackers can ship malicious updates to the agent binary, its plug‑ins, or remote model endpoints, or compromise log collectors that selectively drop or edit entries.
• External compromise. An external adversary that gains local privilege can alter agent configuration, disable or spoof logging, and use the AI layer itself to cover tracks through auto‑cleanup or misleading summaries.

Verdikta’s whitepaper frames an analogous problem at the oracle layer: a single AI oracle becomes a trusted third party that re‑centralizes trust. Agentic OS stacks, as deployed today, recreate that pattern at the endpoint. If an agent becomes the arbiter of which files live or die on disk, we cannot continue to treat its logs as unstructured text; we need verifiable decision provenance.

Centralized OS‑Level Agents Under Dispute: Failure Modes and Limitations

Once OS‑level agents are permitted to delete files, auto‑approve chargebacks, or quarantine processes, they effectively participate in dispute resolution. Analysing their behavior requires the same rigor we apply to financial oracles and arbitration protocols.

The first failure mode is full‑data capture without minimization. Endpoint agents typically see everything on a machine: personally identifiable information, confidential drafts, secrets in memory via accessibility APIs, and transient UI state. Backends frequently persist broad, unredacted telemetry “for debugging.” In a dispute, we rarely need this entire exhaust; we need minimal, well‑scoped evidence bundles. Centralized, unbounded telemetry becomes both a privacy risk and a liability magnet.

The second is opaque decision provenance. Conventional SIEM‑style logs are strings in a database, not cryptographic artifacts. A line such as “Agent classified document X as malicious” is not bound to:

• A hash of the input document bytes.
• The exact model version and hyperparameters.
• The prompt chain or policy set in effect at decision time.

Administrators can edit or delete rows; clocks are mutable; archival processes are opaque. Under forensic scrutiny, such a log line is merely an assertion. There is no way to reconstruct or simulate the decision under identical conditions using verifiable inputs.

The third is brittle recovery. A central orchestrator outage for two hours means either that agents made decisions offline or that no decisions were recorded at all. There is no robust mechanism to reconstruct behavior during that window. A compromised coordinator can silently suppress or rewrite entire classes of events, or accede to targeted pressure to “correct” sensitive histories.

Classical logging fails here because it lacks three properties that Verdikta treats as non‑negotiable for AI arbiters:

1. Hash commitments at decision time. A binding hash of inputs and outputs, created contemporaneously, that cannot be altered ex post without detection.
2. Signed proofs of claims. ECDSA or Ed25519 signatures over those hashes by constrained keys, so that every claim has a cryptographic author and chain of responsibility.
3. Anchoring to an immutable ledger. Time‑stamped commitments posted to an append‑only, Byzantine fault tolerant chain, rather than to a mutable infrastructure database.

Absent these properties, legal and forensic processes devolve into trust arguments. “We do not believe anyone edited these logs” is a statement of policy, not a cryptographic guarantee. When an OS‑level agent classifies a contract as malicious and deletes it, and that decision is later contested, we want an audit trail that looks much closer to Verdikta’s on‑chain record of AI verdicts than to a flat text log file.

Turning Agentic Traces into Decentralized Multi‑Model Verdicts

The technical question is how to wrap agentic OS decisions in a verifiable, decentralized architecture rather than trusting a single coordinator.

Verdikta’s protocol for trustless automated decisions already provides the core machinery. As described in the whitepaper, a requester submits a query—typically a set of IPFS content identifiers (CIDs) and parameters—to an Aggregator contract deployed on Base. The Aggregator consults the Reputation Keeper contract to select a committee of staked arbiters using a mix of on‑chain entropy and arbiter‑supplied randomness, with selection probabilities weighted by reputation and fee. Each arbiter retrieves the evidence from IPFS, evaluates it with one or more AI models, and participates in a two‑phase commit–reveal protocol. The Aggregator clusters revealed likelihood vectors, averages the consensus cluster, records the final scores and concatenated justification CIDs on‑chain, and updates arbiter reputation and rewards.

To extend this mechanism to agentic OS evidence, we introduce an agent connector at the endpoint. This connector is a thin adapter that:

• Observes OS‑level agent actions such as “deleted contract.pdf as malware” or “auto‑denied chargeback request.”
• Captures relevant artifacts: file hashes, minimal content slices, prompts, process trees, and local system context.
• Packages those into a structured evidence bundle: a zipped directory containing a manifest.json and supporting blobs, following the Verdikta user‑guide format where the manifest specifies versions, the primary file, and juryParameters for model selection and weighting.
• Uploads the bundle to IPFS and obtains a CID.

The connector then calls Verdikta’s requestAIEvaluationWithApproval entrypoint, passing an array with that CID and any optional textual addendum. From this point, the standard Verdikta flow applies: a randomized committee of arbiters retrieves the bundle, runs the configured multi‑model evaluation, commits and later reveals their likelihood vectors, and the Aggregator computes a consensus cluster and a final verdict vector.

The resulting dataflow for an agentic OS decision is:

1. Evidence capture. The agentic OS emits an action; the connector constructs an evidence bundle capturing inputs and context.
2. On‑device attestation. The device signs a Merkle root of the bundle (discussed below) and embeds an attestation token in the manifest.
3. Commit. Either the connector or, more typically, the arbiters post hash commitments to the Verdikta Aggregator on Base.
4. Off‑chain multi‑model evaluation. Arbiters evaluate the bundle using heterogeneous models as specified in juryParameters—for example, OpenAI and Anthropic models plus open‑source baselines, each with assigned weights.
5. Reveal and aggregation. Arbiters reveal their likelihood vectors and salts; the Aggregator verifies commitments, clusters responses, and computes the aggregated verdict.
6. On‑chain verdict. The Aggregator records the final scores and justification CIDs and emits a FulfillAIEvaluation event on Base, which downstream contracts or monitoring systems consume.

The trade‑off is clear. This introduces additional latency—on the order of minutes, consistent with Verdikta’s sub‑two‑minute target for typical disputes—and some oracle gas cost on Base. In exchange, critical agentic OS decisions obtain a durable, decentralized verdict anchored in cryptographically committed evidence, rather than an opaque, mutable log entry.

Cryptographic Primitives for Verifiable Agent Logs and Claims

To make agent logs withstand adversarial scrutiny, we must replace free‑form text entries with cryptographically verifiable claims and evidence bundles.

Verdikta’s commit–reveal protocol provides a template. In the commit phase, each arbiter computes

commitHash = bytes16(SHA-256([sender, likelihoods, salt]))

where salt is an 80‑bit random value. Arbiters post only commitHash on‑chain. In the reveal phase, they submit (likelihoods, salt); the Aggregator recomputes the hash and rejects any mismatch. This prevents an arbiter from waiting to see others’ answers and then copying or modifying its own.

An agent connector can apply the same pattern to local claims. For each high‑stakes action, it constructs a canonical JSON document containing, at minimum:

• input_root: a Merkle root over the relevant artifacts (document hash, prompt hash, process tree, selected logs).
• model_id and model_version for the OS‑level agent.
• policy_id and configuration snapshot.
• decision and any associated scores.
• timestamp and host_id.

The connector signs the hash of this payload using an ED25519 or ECDSA key bound to the device (for example, via TPM or Secure Enclave). Multiple claims for a single action can be combined into a Merkle tree, whose root becomes the canonical evidence hash. That root is what flows into Verdikta: it appears in the manifest.json of the evidence bundle and is ultimately referenced in the on‑chain verdict.

For consensus outputs, threshold signatures or multisignatures reduce on‑chain verification cost. Instead of recording N independent arbiter signatures, arbiters participate in a BLS threshold scheme (for instance, over BLS12‑381). Once a threshold of partial signatures is collected off‑chain, they aggregate into a single signature over the tuple (evidence_root, verdict_vector, justification_cids, timestamp). The Aggregator verifies this single signature and stores its hash. Contracts and verifiers downstream perform one pairing check rather than N ECDSA verifications.

Privacy‑preserving proofs fill the remaining gap. Many agentic OS artifacts are sensitive or regulated. We do not want raw content on‑chain, and in some cases we do not want arbiters to see full plaintext either. Here, we rely on content‑addressed storage and zero‑knowledge proofs:

• Evidence bundles reside off‑chain on IPFS or Arweave, referenced by CIDs embedded in the manifest.
• Circuits compiled with zk‑SNARK toolchains such as Circom or Halo2, or zk‑STARK frameworks, prove that a hidden document with Merkle root R satisfies a predicate P—for example, “contains a specific contractual clause” or “matches this classification under a certified model.”
• On‑chain, contracts see R, a verdict vector, and a succinct proof. They do not see the underlying bytes.

The gas and latency trade‑offs are manageable. Hashes, verdict vectors, and (optionally) aggregated signatures live on Base L2, where gas is relatively inexpensive; large payloads and proofs remain off‑chain. Proof generation adds CPU time off‑chain, but verification is efficient. Overall, this pattern aligns with Verdikta’s existing design: keep large artifacts off‑chain behind CIDs, and store only commitments and final outcomes on‑chain.

Attestation and Tamper‑Resistant Evidence Capture at the Endpoint

Cryptographic structure is insufficient if the evidence itself can be fabricated. For agentic OS deployments, we also need assurance that the connector capturing evidence is running unmodified on a sane platform.

On devices with trusted execution environments, the connector can leverage hardware attestation:

• Intel SGX/TDX and ARM TrustZone provide enclaves where the connector runs, with the ability to produce attestation quotes binding a public key to a measured code identity.
• Apple Secure Enclave or platform TPMs generate keys that never leave the enclave; those keys sign evidence roots.
• WebAuthn attestation ties keys to TPM‑backed credentials in browser or desktop contexts.

A typical capture flow is then:

1. The connector obtains a hardware attestation token binding a public key K_device to a measured code hash and platform identity.
2. For each agentic action, it computes a Merkle root R_evidence over the selected artifacts.
3. It signs (R_evidence, timestamp, agent_id) with K_device.
4. It embeds both the attestation token and the signature in the manifest.json of the evidence bundle before uploading to IPFS.

When arbiters later process the bundle as part of a Verdikta evaluation, they verify the attestation token and the signature prior to trusting R_evidence. The commitment anchored on Base is thus a hash over attested evidence, not arbitrary bytes produced by a potentially compromised process.

In non‑TEE environments, we can fall back to software attestation and federated attesters. The connector publishes its binary hash, configuration, and environment to a remote attestation service; multiple independent attesters—an internal security team and an external auditor, for example—co‑sign statements that specific hashes and configurations are acceptable. These statements and signatures accompany the evidence bundle. The trust model is weaker than with hardware TEEs, but still strictly stronger than unverified local logs.

In both cases, the binding to Base L2 completes the chain of custody. The Verdikta Aggregator records a commitment that depends on the attested evidence root and the arbiter responses. Any subsequent deviation—in device measurements, connector binaries, or arbiter behavior—manifests as a mismatch between new measurements and previous, time‑stamped commitments.

Oracle Integration, Settlement on Base L2, and Practical Deployment

For this architecture to be operationally useful, agentic OS evidence and Verdikta verdicts must be integrated into existing payment and control flows.

Verdikta already uses Chainlink as the oracle layer between its on‑chain Aggregator and off‑chain arbiter nodes. The commit and reveal phases are implemented as two rounds of oracle calls: the Aggregator emits tasks, Chainlink nodes fetch IPFS evidence, perform AI evaluations, and call back with commitments, then later with reveals. Users fund evaluations in LINK, and the Aggregator disburses base fees and bonuses according to the incentive scheme specified in the whitepaper.

An agentic OS connector need only participate at the boundary. It uploads evidence bundles to IPFS, obtains CIDs, and calls requestAIEvaluationWithApproval on the Verdikta Aggregator deployed on Base, after approving sufficient LINK spend. The SDK patterns shown in the Verdikta User’s Guide—off‑chain funding approval in JavaScript, and on‑chain invocation from Solidity contracts—apply unchanged.

Chainlink Functions running within arbiter nodes can validate bundle structure and attestation tokens prior to model evaluation. They can also orchestrate calls to external AI APIs or local models as specified in juryParameters. From a payment perspective, nothing new is required: arbiters continue to earn base fees for commits and bonus multipliers for clustered reveals, as in the existing dispute‑resolution use cases.

Base L2 serves as the canonical ledger for verdict events. Other EVM chains or rollups that care about the outcome can consume it via cross‑chain oracles. For example, an escrow contract on another L2 can treat local OS‑agent decisions as optimistic hints. If a user disputes an auto‑deleted invoice or denied claim, the contract triggers a Verdikta evaluation on Base. Once a FulfillAIEvaluation event with the relevant aggregation ID appears, the contract either finalizes or reverses the tentative action based on the verdict vector.

Gas optimization follows Verdikta’s established pattern. Only commitments, final likelihood vectors, and justification CIDs are stored on‑chain; bulk evidence and attestation tokens remain off‑chain behind CIDs. Where latency and cost permit, multiple low‑stakes agentic events can be batched into one evidence bundle and one Verdikta request. High‑stakes events—such as deletion of legal documents or irreversible financial actions—justify their own evaluations.

Developer Roadmap: From Agent Logs to On‑Chain Verdicts

To move from architecture to implementation, a pragmatic pilot roadmap for agentic OS integration can be executed in roughly three months.

The first step is to define interoperable agent interfaces that emit verifiable logs. Concretely, specify a JSON‑LD schema for agent decisions with fields for input_root, model_id, model_version, policy_id, decision, scores, timestamp, and host_id. Bundle additional artifacts into a Merkle tree and include its root in every claim. Require each agentic component to sign claims with a device‑ or process‑bound key, and, where available, attach attestation tokens.

The second step is to ship an SDK. Provide libraries in Node.js, Python, and a native Windows service that can observe agentic OS events, build Verdikta‑compatible evidence bundles (zipped directories with manifest.json and supporting files), negotiate attestation where hardware permits, upload bundles to IPFS, and call requestAIEvaluationWithApproval on Base. Expose hooks for applications to receive callbacks when a Verdikta verdict event finalizes.

Third, implement a Windows connector pilot for a constrained domain such as file quarantine and deletion decisions. Use a lightweight relayer service to batch on‑chain submissions on behalf of endpoints, managing private keys and gas budgets centrally while preserving per‑endpoint signatures inside the evidence bundles.

Fourth, configure Chainlink oracle jobs dedicated to this flow. These jobs should validate bundle structure and attestations, then run the specified models as part of arbiter evaluation. They can also implement policy checks—for example, ensuring that a given class of action always triggers a multi‑model evaluation rather than relying solely on the local agent.

Fifth, prototype verifiable evidence bundles and threshold‑signed on‑chain verdicts. Over the pilot period, measure end‑to‑end latency from agent action to on‑chain verdict, gas cost per evaluation on Base, and the disagreement rate between local agent decisions and Verdikta verdicts. Treat disagreements as valuable signals: they identify misaligned policies, model drift, and ambiguous cases that merit human review.

Finally, push for standards and policy support. Technically, we need open standards for explainability APIs and signed provenance headers for agentic actions, and interoperable attestation formats that span TEEs and software attesters. Legally and regulatorily, there is a strong argument that cryptographically auditable agent logs, anchored by protocols such as Verdikta, should be considered a higher‑assurance baseline than opaque, centralized logging stacks.

From an implementation standpoint, this architecture enables agentic OS deployments that are not only powerful but accountable. The design choice is explicit: accept some additional complexity in SDKs, attesters, and oracle jobs, and some latency overhead per evaluation, in exchange for deterministic, auditable verdicts with a clear chain of cryptographic responsibility. When the next incident‑response lead is asked in a deposition how they know their AI agent did not simply erase the evidence, they can point not to mutable SIEM rows, but to a Base L2 transaction containing an attested evidence hash, a decentralized panel’s verdict, and content‑addressed justifications that still resolve.

The technical implications extend beyond agentic OS deployments. The same pattern—evidence bundles, attestation, commit–reveal, multi‑model consensus, and on‑chain verdicts—applies to programmable escrow, content moderation, and DAO policy enforcement. In all of these domains, the objective is the same: trust at machine speed, with cryptographic guarantees that survive adversarial scrutiny.