Opinion: Jesús Rodriguez, Sentora Co-Founder
AI for coding has achieved product market fit. Web3 is no exception. Among the areas where AI will permanently change, smart contract auditing is particularly ripe for transformation.
Today’s audits are fleeting, point-in-time snapshots that struggle in configurable and hostile markets and often miss economic failure modes.
The center of gravity is shifting from artisanal PDFs to continuous tool-based assurance: solvers, fuzzers, simulations, and models combined with live telemetry. Teams that adopt this will be able to ship faster while covering a wider area. A team that does not run the risk of not being listed or being insured.
Audits aren’t as common as you think
The audit became Web3’s de facto due diligence ritual, tangible evidence that someone tried to break the system before the market did. However, this ceremony is a product of the pre-DevOps era.
Traditional software built guarantees into pipelines such as testing, continuous integration/continuous deployment gates, static and dynamic analysis, canaries, feature flags, and deep observability. Security works like a micro-audit on every merge. Web3 brought back explicit milestones, as immutability and adversarial economics removed the rollback escape hatch. The obvious next step is to integrate platform practices with AI and ensure that guarantees are always enabled rather than a one-time event.
Limitations of smart contract auditing
Auditing buys time and information. These force teams to clarify invariants (store of value, access control, ordering), test assumptions (oracle integrity, upgrade privileges), and pressure test failure boundaries before capital lands. A good audit leaves behind assets such as threat models that survive versions, executable properties that become regression tests, and runbooks that make incidents boring. Space must evolve.
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The limits are structural. Auditing freezes a living configurable machine. Upstream changes, liquidity shifts, maximum extractable value (MEV) tactics, and governance activities can invalidate yesterday’s guarantees. Scope is limited by time and budget, and efforts are biased toward known bug classes, while emergent behaviors (bridges, reflexive incentives, and interactions between decentralized autonomous organizations) take a backseat. Because the start date compresses the triage process, reporting can create a false sense of closure. The most damaging failures are often economic rather than syntactical, requiring simulation, agent modeling, and runtime telemetry.
AI is not yet good at coding smart contracts
Modern AI thrives in environments rich in data and feedback. Compilers provide token-level guidance, and models now scaffold projects, translate languages, and refactor code. Engineering smart contracts is even more difficult. Righteousness is temporary and hostile. In Solidity, safety depends on execution order, attacker presence (reentrancy, MEV, frontrunning, etc.), upgrade path (including proxy layout and delegate call context), and gas/refund dynamics.
Many invariants span transactions and protocols. In Solana, the account model and parallel runtime add constraints (PDA derivation, CPI graph, computational budget, rent forgiveness balance, serialization layout). These properties are missing from the training data and difficult to obtain with unit tests alone. Current models fall short here, but with better data, stronger labels, and tool-based feedback, the gap can be engineered.
A practical path to becoming an AI auditor
A working build path consists of three main elements.
First is the audit model. It hybridizes large language models with symbolic and simulation backends. Let the model extract intent, suggest invariants, and generalize from idioms. Allows solvers/model checkers to provide guarantees through proofs or counterexamples. Search requires suggestions based on audited patterns. The output artifacts should be specifications with proofs and reproducible exploit traces, not convincing prose.
The agent process then coordinates a special agent called a property miner. A dependency crawler that builds risk graphs across bridges/oracles/vaults. Red Team with Menpool in mind looking for minimum capital exploits. Economic managers who emphasize incentives. Upgrade director rehearsing canary, time lock, and kill switch training. Additionally, it includes a summarizer to create governance-ready briefings. This system operates like a nervous system, continuously sensing, reasoning, and acting.
Finally, there is the evaluation that evaluates what is important. Beyond unit tests, we track property coverage, counterexample yield, state space novelty, time to discovery of economic failure, minimum exploit capital, and runtime alert accuracy. In addition to detection, public incident-derived benchmarks should score the quality of a set of bugs (reentrancy, proxy drift, oracle skew, CPI abuse) and triage. Assurance becomes a service with explicit service level agreements and artifacts that insurers, exchanges, and governance can rely on.
Make space for generalist AI auditors
While a hybrid path is attractive, scale trends suggest another option. In adjacent domains, generalist models that coordinate tools end-to-end are comparable to or better than specialized pipelines.
For auditing, a fully-featured model with long contexts, robust tool APIs, and verifiable outputs can internalize security idioms, infer long traces, and treat solvers/fuzzers as implicit subroutines. Combined with long-term memory, it can create properties, suggest exploits, perform searches, and describe fixes in a single loop. Still, anchors (proofs, counterexamples, supervised invariants) are important, so pursue hybrid sanity now while seeing if generalists will collapse part of the pipeline tomorrow.
AI smart contract audits are inevitable
Web3 combines immutability, composability, and adversarial markets. In other words, the state space shifts from block to block in an environment where temporary artisanal auditing cannot handle it. AI is great when the code is rich, the feedback is dense, and the validation is mechanical. Those curves are converging. Whether winning takes the form of today’s hybrid or tomorrow’s generalist, by aligning tools end-to-end, assurance is moving from milestone to platform, continuous, machine-enhanced, and anchored by proofs, counterexamples, and monitored invariants.
Treat your audit as a product, not a deliverable. Start hybrid loops (CI executable properties, solver-aware assistants, memorypool-aware simulations, dependency risk graphs, immutable sentinels) and compress your pipeline as your generalist model matures.
AI-enhanced assurance is more than just checking a box. It is built into the operational capabilities of a configurable adversarial ecosystem.
Opinion: Jesús Rodriguez, Sentora Co-Founder.
This article is for general informational purposes only and is not intended to be, and should not be taken as, legal or investment advice. The views, ideas, and opinions expressed herein are those of the author alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.