Agentic AI is starting to take on a much bigger role in crypto security, moving beyond simple alerts and analysis to actively investigating threats, tracing stolen funds and responding to incidents with limited human involvement.
According to a new CertiK Intel3D report, autonomous AI systems are increasingly being used across cybersecurity, anti-money laundering and compliance operations.
Unlike traditional AI tools that mainly flag suspicious activity or summarize data, agentic AI can work through multiple steps, interact with external tools and APIs, gather evidence and take actions in live environments.
CertiK describes this emerging model as an AI-powered security workforce in which autonomous systems operate within clearly defined roles while human experts focus more on oversight, verification and accountability.
Agentic AI is moving from assistant to operator
For years, artificial intelligence played a supporting role in cybersecurity.
Machine learning systems were typically used to detect unusual behavior, while natural language processing tools helped analysts summarize alerts and prepare reports.
The final decision, however, usually remained in human hands.
Agentic AI changes that model.
CertiK says autonomous systems can now complete several stages of an investigation on their own.
For example, an AI agent investigating a suspicious login could retrieve device information and location history, compare that data with threat intelligence, decide whether the account should be suspended and then carry out the suspension.
The system could also record its reasoning and actions for later human review.
Because of this increased independence, CertiK argues that organizations should begin treating AI agents more like workforce participants with defined responsibilities rather than conventional software tools.
Human accountability, however, remains essential.
AI systems themselves do not carry legal or operational responsibility. Organizations deploying them, along with the people configuring and supervising their behavior, remain responsible for the final outcome.
Crypto attacks are pushing security teams toward automation
One of the biggest reasons for adopting autonomous security systems is the speed of modern crypto attacks.
Flash loan exploits can drain decentralized finance protocols within seconds.
Stolen assets can also move rapidly through multiple wallet addresses, blockchain bridges and mixing services, making it difficult for human investigators to respond quickly enough.
Crypto security losses remain substantial.
CertiK data cited in the report showed approximately $768.4 million in losses during September across 97 incidents.
Total crypto-related losses under the firm’s methodology reached roughly $2.68 billion by the end of September 2026.
The growing speed and complexity of these attacks creates strong demand for systems that can detect and respond to suspicious behavior in real time.
AI agents are taking on smart contract security
Smart contract security is one of the areas where CertiK expects agentic AI to have a significant impact.
Traditional smart contract audits usually combine manual code reviews with tools such as static analysis, symbolic execution and fuzzing.
These tools can identify suspicious code or potential vulnerabilities, but experienced auditors normally need to determine whether a finding represents a genuine security risk.
Agentic AI can go further.
Autonomous systems can analyze a smart contract’s call graph, examine state changes across multiple contracts and inspect external interactions.
They can also look for vulnerabilities involving:
- Reentrancy attacks
- Oracle manipulation
- Access control weaknesses
- Unsafe upgrade mechanisms
CertiK says AI is also being applied to formal verification.
Agents may generate formal specifications and test those specifications against actual contract behavior, reducing some of the manual workload traditionally handled by formal methods engineers.
Human auditors are still expected to remain involved.
Their role is increasingly shifting toward checking AI-generated findings, investigating new economic and game-theoretic attack techniques and identifying blind spots that automated systems may miss.
AI can monitor blockchain transactions in real time
Agentic AI is not limited to audits performed before a smart contract is deployed.
CertiK expects autonomous systems to play a growing role in monitoring active blockchain networks.
AI systems can watch pending and confirmed transactions for signs of:
- Flash loan attacks
- Oracle manipulation
- Abnormal liquidity withdrawals
- Other exploit patterns
In more advanced setups, detecting an attack could immediately trigger an automated response.
An agent might pause a vulnerable smart contract function, activate a circuit breaker or freeze a compromised administrator key without waiting for a human operator.
This type of response could be especially important when an exploit is unfolding within seconds.
AI could trace stolen crypto across multiple chains
Fund tracing is another major area where autonomous systems could improve crypto investigations.
Traditional investigators often have to manually follow stolen funds from one address to another.
That process becomes increasingly difficult when assets are split across different wallets, passed through mixers, moved between exchanges or transferred across blockchain bridges.
CertiK says agentic systems can continuously monitor stolen assets while they move.
Instead of reconstructing a transaction trail after the funds have already disappeared, AI agents could follow activity in real time.
They can also update wallet clusters as new activity appears.
For example, autonomous systems may analyze transaction timing, shared counterparties and gas fee behavior to determine whether several addresses are likely controlled by the same entity.
Cross-chain transactions add another layer of complexity.
Historically, many blockchain monitoring systems analyzed individual networks separately.
Agentic AI can combine activity across several chains into a single investigation and continue tracking assets as they move through bridges or cross-chain swaps.
CertiK pointed to laundering linked to the Bybit exploit as an example.
Previous company research found that 86.29% of the stolen ETH involved in the incident was converted into Bitcoin within one month using mixers, blockchain bridges and over-the-counter brokers.
AI agents could automate crypto compliance
The rise of agentic AI could also reshape crypto compliance.
CertiK says Know Your Address and Know Your Transaction screening can increasingly be performed automatically before transactions settle.
AI agents can evaluate an address or transaction in real time and determine whether it may be connected to illicit activity.
That could allow platforms to identify potentially risky transfers before completing them.
Regulatory reporting is another area that could become more automated.
Agentic systems can combine blockchain data with off-chain records and prepare reports related to requirements such as Travel Rule data sharing and stablecoin reserve attestations.
The regulatory burden facing crypto companies has already increased significantly.
An earlier CertiK Skynet report found that anti-money laundering penalties exceeded $900 million during the first half of 2025 as regulators increasingly moved from developing crypto rules toward enforcing them.
AI agents are becoming blockchain users themselves
An entirely new compliance challenge appears when autonomous AI agents begin interacting directly with blockchain networks.
These agents can potentially hold crypto assets, execute trades, manage treasury operations and interact with decentralized finance protocols.
That means companies may eventually need to monitor and audit the blockchain activity of their own AI systems.
Organizations may need records explaining:
- What information an AI agent used
- How it reached a decision
- What action it performed
- Whether human approval was involved
This scenario is already beginning to move beyond theoretical research.
MetaMask launched an AI Agent Wallet in June that allows autonomous agents to perform actions such as token swaps and perpetual futures trades under controls set by users.
Autonomous AI introduces new security risks
Giving AI agents more authority also creates new attack surfaces.
CertiK warns that autonomous systems can still produce incorrect information while appearing highly confident.
In AML investigations, for example, an AI-generated Suspicious Activity Report could contain an inaccurate transaction trail.
During a smart contract audit, an agent could incorrectly determine that a vulnerability has been prevented.
Human reviewers may also become less likely to catch these mistakes if they gradually develop too much confidence in automated systems.
Attackers can exploit many of the same AI capabilities.
CertiK says threat actors are already using artificial intelligence to accelerate vulnerability discovery, automate reconnaissance and create more convincing social engineering campaigns.
The company has also previously warned that AI-generated phishing, deepfakes and automated exploit tools are making attacks increasingly difficult to detect.
Security agents could themselves become attack targets
One of the biggest concerns is that security-focused AI agents may have access to highly sensitive systems.
If an attacker successfully manipulates an agent’s inputs, the consequences could be serious.
CertiK highlighted prompt injection as one potential threat.
A compromised agent could potentially be manipulated into approving a fraudulent transaction, disabling a legitimate security control or performing another unauthorized action.
The more authority an autonomous agent receives, the more important it becomes to protect the agent itself.
Human oversight remains essential
Despite the potential benefits of autonomous security systems, CertiK stresses that responsibility must remain with people and organizations.
Legal liability for harmful autonomous actions remains unclear across many jurisdictions.
For that reason, companies deploying AI agents need to clearly define what each system is allowed to do and when human approval is required.
CertiK recommends several safeguards, including:
- Maintaining detailed audit trails
- Recording agent inputs, reasoning and actions
- Setting strict limits on autonomous authority
- Creating clear escalation procedures
- Testing agents against adversarial manipulation
- Assigning a named human owner to every autonomous system
These controls become increasingly important as agentic AI moves from offering recommendations to making real-world decisions.
Agentic AI could reshape crypto security
Agentic AI represents a significant shift in how cybersecurity, blockchain monitoring and compliance work could be handled.
Instead of simply assisting human analysts, autonomous systems are increasingly capable of investigating incidents, analyzing smart contracts, tracing stolen assets and responding to threats in real time.
That could help security teams react faster to increasingly sophisticated crypto attacks.
At the same time, greater autonomy introduces its own risks.
AI systems can make mistakes, be manipulated and potentially take damaging actions if their authority is not carefully controlled.
For crypto companies, the challenge will be finding the right balance between automation and human oversight.
As autonomous agents take on a larger share of security work, organizations will need clear boundaries, complete audit trails and strong accountability structures to ensure that faster security does not create an entirely new class of vulnerabilities.
Disclaimer: This article is for informational and educational purposes only and should not be considered financial, legal or cybersecurity advice.
