Why Agentic AI Is Non-Deterministic (and Why That Matters)

Traditional code is deterministic: the same input always produces the exact same output. Agentic AI, powered by large language models (LLMs), is not. LLMs generate responses probabilistically – they sample from a distribution of possible tokens based on patterns learned during training. Even with identical prompts, outputs can vary due to:

• Temperature and sampling parameters
• Multi-step reasoning loops (planning → tool selection → execution → re-evaluation)
• Hallucinations or subtle prompt sensitivities
• External tool results that feed back into the next reasoning step

When an agent is given the ability to act (send an email, post in Slack, query a database, or trigger a business process), this non-determinism creates real risk. A seemingly harmless prompt can lead to prompt injection, tool misuse, unintended data exposure, or actions outside the intended scope. The system is no longer a predictable calculator – it becomes an autonomous decision-maker operating in a probabilistic space.

That is exactly why Agentic AI must be controlled tightly. Without deliberate, layered safeguards, the blast radius of a single unexpected output can be 

Two Secure Paths to Agentic AI

We guide clients based on their needs and technical maturity:

1. Vendor-Managed Walled-Garden Platforms (ideal for non-technical teams)
For business users who need AI chat, workflows, or integrations (Slack, Gmail, internal docs) without building infrastructure, we recommend fully sanctioned, vendor-managed platforms that meet our strict compliance bar:

Key requirements we enforce:

• ISO 27001, SOC 2 Type II
• Full GDPR alignment
• EU hosting
• No training on customer data
• Transparent sub-processors and DPA
• Built-in (or easily configurable) human-in-the-loop (HITL), confirmation steps, and basic guardrails

Currently under final evaluation:

• Langdock (Berlin/Azure EU):  strong for workflows and agents with Guardrails node, per-action confirmation (HITL), and admin controls.
• Lurus (Hannover):  affordable multi-agent automation for chat-initiated tasks.

The core rule is simple: stay inside the platform. The vendor owns the security model –  but only within their boundary. We help clients configure these platforms (admin hardening, workflow design, HITL policies) so they remain safe and auditable.

2. Custom Bounded Agents (for teams needing maximum control)
When pre-built integrations aren’t enough, we develop fully controlled agents using a robust defense-in-depth stack:

• Orchestration with LangGraph and Inngest
• Guardrails AI for robust validation
• Lasso for traffic inspection
• ToolHive for per-tool isolation

These agents feature narrow tool scopes, comprehensive auditing, and human oversight gates — delivering precision and security.

Datarella’s Agentic AI Services

• Strategy & Architecture: Risk assessment and governance frameworks
• Platform Selection & Hardening: Compliant vendor evaluation and secure configuration
• Custom Agent Development: Production-grade bounded agents
• Tool & MCP Security:  Secure integration of external capabilities
• Enablement & Assurance: Team training, guidelines, and ongoing audits

Why Partner with Datarella?

Agentic AI unlocks major productivity gains, but its non-deterministic nature demands rigorous control. We combine deep expertise with practical, EU-compliant solutions so you can deploy agents confidently –  whether through trusted vendor platforms or tightly bound custom systems.

Ready to implement secure Agentic AI? Contact Datarella today to discuss your use case.

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In April 2026, two timely pieces cut through the AI hype cycle. Ethan Mollick, writing in The Economist, warned that “the IT department [is] where AI goes to die.” His core argument: AI is a profoundly odd, risky, and powerful technology, a next-word predictor that unexpectedly writes code, gives strategic advice, or shows empathy, and companies are killing its potential by trying to “de-weird” it. Traditional IT processes, risk-averse governance, standardized KPIs, and legacy systems force AI into the mold of conventional enterprise software, stifling experimentation and emergent value. 

At the same time, the Financial Times published “Investors are betting on AI chaos. History suggests otherwise.” Author Richard Waters noted that markets are pricing in revolutionary disruption – new winners, old losers – but past technology revolutions (PCs, internet, cloud) show savvy incumbents often muddle through, adapt, and even thrive. The real story is rarely the total chaos investors crave. 

Together, these perspectives paint a clear picture of the most likely future of AI in business: not a dystopian job apocalypse or unicorn-disrupting chaos, but a pragmatic, evolutionary integration. AI will augment human work, reshape workflows, and deliver real value, but only for organizations that treat it as the strange, probabilistic tool it is while building the right foundations. Incumbents with strong data capabilities will have the edge.

Why AI “Dies” in Traditional IT, and Why That’s the Wrong Approach

Mollick’s essay resonates because it diagnoses a widespread problem we see daily in enterprise deployments. AI isn’t deterministic software with predictable outputs. It’s generative, context-dependent, and often surprising. When companies hand it to IT teams focused on security, compliance, uptime, and cost control, the natural response is to:

• Wrap it in rigid approval processes
• Demand ROI projections before pilots
• Force it into existing tech stacks without workflow redesign
• Prioritize “safe” use cases over creative experimentation

The result? Pilots that never scale. According to recent analyses (including Deloitte’s 2026 State of AI in the Enterprise), while worker access to AI tools has surged, the number of organizations moving projects into full production remains modest. Barriers like data quality, skills gaps, infrastructure readiness, and risk management continue to stall progress.

HBR has similarly observed that many companies report “widespread AI usage but disappointing returns,” with adoption stalling at the integration stage. The problem isn’t execution – it’s treating AI like a new CRM module instead of a fundamentally new way of working. 

History Shows Incumbents Can Win If They Adapt Smartly

The FT piece offers reassurance: AI won’t necessarily destroy every incumbent. Past waves of technology (from electricity to the internet) initially sparked predictions of massive disruption, yet established players who invested in complementary capabilities—new skills, processes, and organizational structures—came out stronger.

In 2026, the winners won’t be the pure-play AI startups alone. They will be enterprises that:

• Combine their domain expertise and proprietary data with AI’s capabilities
• Redesign workflows around human-AI collaboration (what some call “co-intelligence”)
• Scale from pilots to enterprise-wide agentic systems under proper guardrails

PwC’s 2026 AI Business Predictions and similar reports emphasize a “disciplined march to value”: top-down enterprise strategies, measurable business outcomes, and governance that doesn’t kill experimentation. 

The Most Likely Future: Pragmatic, Data-Driven, and Agentic

By late 2026 and into 2027, we expect the following trajectory:

1. From pilots to production at scale — Organizations doubling the share of AI projects in production, driven by agentic AI (autonomous agents that execute multi-step workflows).
2. J-curve productivity — Initial flat or negative returns as companies rewire processes, followed by steep gains once complementary innovations (new roles, data pipelines, decision protocols) are in place.
3. Governance catching up — Mature frameworks for agentic AI, data quality, and responsible use becoming table stakes. Shadow AI will decline as secure, enterprise-grade platforms mature.
4. Incumbents leveraging data moats — Companies with clean, governed data and domain expertise will outperform pure AI-native players in regulated or complex industries.

This future is neither utopian revolution nor failure – it’s an evolutionary transformation, provided organizations avoid the “IT department trap.”

Optimal Use of AI in Businesses: Five Practical Principles

Drawing from Mollick, historical lessons, and 2026 enterprise reports (Deloitte, McKinsey, PwC), here’s how forward-thinking companies are winning:

1. Embrace the weirdness—experiment deliberately
   Give teams space to discover unexpected uses. Mollick advocates leadership that encourages crowdsourced experimentation and “labs” to scale promising ideas. Treat AI like a creative collaborator, not just an automation tool.
2. Build on rock-solid data foundations
   Data quality and governance remain the #1 barrier cited across reports. Without trustworthy data pipelines, AI outputs are unreliable. This is where specialized partners excel – unifying siloed data, implementing real-time pipelines, and ensuring privacy/compliance.
3. Redesign workflows and roles around human-AI co-intelligence
   Don’t automate jobs – augment them. Successful organizations are re-architecting processes so humans focus on judgment, creativity, and relationships while AI handles analysis, drafting, and routine execution.
4. Deploy secure, governed agentic AI
   Autonomous agents are the next frontier, but they require bounded orchestration, threat modeling, and compliance-by-design. Enterprises need platforms that support multi-agent systems without introducing new risks.
5. Measure what matters – and iterate
   Move beyond vanity metrics. Track business outcomes (revenue impact, cost savings, customer satisfaction) and accept that ROI may follow a J-curve.

How Datarella Helps Businesses Navigate This Future

At Datarella, we’ve spent years helping organizations move beyond AI hype and pilot purgatory. Our expertise in AI agent development and security, full-stack application modernization, Web3-enabled decentralized solutions, and privacy-preserving data architectures directly addresses the challenges outlined above.

Whether you need:

• Secure, production-ready autonomous agents
• Data platforms that make AI reliable and compliant
• Integration of AI into legacy systems without the usual friction
• Or decentralized approaches that enhance trust and data integrity

We combine deep technical capability with practical business understanding to help you safely and scalably embrace AI’s weirdness.

The future of AI in business isn’t about replacing your IT department or betting everything on chaos. It’s about evolving how your organization learns, decides, and creates value—by treating AI as the strange, powerful tool it is, while building the data, governance, and cultural foundations it demands.

Ready to move from pilots to production without letting AI “die in IT”? Let’s talk. Contact Datarella to explore how we can help you capture the real, pragmatic upside of AI in 2026 and beyond.

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Similar to participants in decentralized Web3 networks, individual citizens in Europe enjoy a high degree of autonomy, freedom, and self-determination. While this autonomy is inherently embedded in the software code of Web3, in Europe, national governments create the legal frameworks. Examples like eIDAS and Self-sovereign Identities (SSI) establish EU-wide standards that enable secure cross-border digital transactions.

At Datarella, we have actively participated in decentralized systems through our projects, most recently in the GAIA-X funding project moveID. The experiences gained lead to two key conclusions: The values and benefits of decentralized systems are recognizable and measurable, offering flexibility and innovation in dynamic environments. However, decentralized systems are not feasible or value-creating without a direct connection to centralized elements. This may sound contradictory at first, but it is not.

The Necessity of Centralized Elements in Decentralized Systems

Decentralized systems do not develop from within themselves; they always require a central idea or organization as the initial spark. Furthermore, a central entity must permanently handle tasks in governance, administration, and management. Without this, decentralized systems tend toward apathy or inactivity, as current incentive models do not ensure long-term constructive activity. A decentralized system remains active only as long as central functions provide the necessary incentives. Additionally, basic infrastructure must be created and operated – a task typically handled centrally, with costs distributed among participants.

From the perspective of organization theory, this aligns with contingency theory: There is no universally best structure; the choice between central and decentralized depends on the environment. In stable contexts, centralized systems provide efficiency and control, while decentralized ones promote agility in volatile markets. Henry Mintzberg describes in his organizational models that centralized structures (e.g., Machine Bureaucracy) are suitable for standardization, whereas decentralized ones (e.g., Adhocracy) foster innovations. Disadvantages of centralized systems include the lack of flexibility, while decentralized systems can lead to coordination issues.

Symbiosis as the Path to Success

In short, decentralized and centralized systems can form a beneficial symbiosis that compensates for the drawbacks of monolithic approaches and generates more prosperity for all participants. Hybrid models, as recommended in organization theory, combine stability with agility and are exceptionally sensitive in complex environments.

A necessary prerequisite for this symbiotic interplay is the ability and willingness of participants to understand the advantages and limitations of each system, along with the commitment to contribute to governance constructively. Only then do the positive outcomes emerge. Participants who see only the benefits of a monolithic structure should be excluded to maintain integrity.

At Datarella, we apply these insights in our data-driven solutions for health and sustainability, developing hybrid systems that link autonomy with reliable governance.

Do you have experience with such structures? Please share them in the comments!

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Cyber-Physical Trust: The Future of M2M Communication

We’re revolutionizing the security landscape by integrating blockchain technology and M2M communication to provide trust and authenticity. In addition, blockchain technology ensures the integrity and transparency of digital twins, enabling secure interactions between machines and humans. As a result, we envision a future where cyber-physical trust becomes the standard, surpassing traditional processes. Notably, this shift will have a significant impact on the way we approach security.

Firm-Specific Track & Trace Tools

We empower companies to create applications without requiring extensive technical knowledge. Specifically, our SDK seamlessly integrates into existing infrastructure and embeds the digital twin with necessary code for secure interactions. Furthermore, we provide comprehensive setup guides and API references for smooth onboarding. Meanwhile, developers can choose their desired authentication hardware, extract the required code, and embed it into the app environment. Subsequently, our system tracks digital twin usage, maintaining high security. In the future, we plan to implement additional business logic via smart contracts, enabling data provision and payments. Additionally, event tracking creates an immutable record for decision-making.

The Future of Trust and Authenticity in Digital Twin Security

To summarize, digital twins are essential for real-time monitoring, analysis, and optimization of physical objects. As their use increases, it becomes clear that robust security measures are needed. In response, our innovative approach secures digital twins with advanced event-tracking technology, integrating blockchain and M2M communication to ensure trust and authenticity. Moreover, we provide an SDK that integrates into existing infrastructure, allowing companies to create secure applications easily. Ultimately, our system tracks digital twin usage to maintain high security, with plans for smart contracts and payments. In conclusion, event tracking creates an immutable record for informed decision-making.

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Two European non-profit organizations, INATBA and EBA, have unveiled the Report on AI and Blockchain Convergence. This report serves as a starting point for further discussions on the tensions surrounding Web3.

What’s in the Report?

The convergence of Web3 and AI can help segregate authentic from inauthentic content. Blockchain technology has matured beyond its initial cryptocurrency applications. It is now a fundamental tool in enhancing security, transparency, and efficiency across various industries. Blockchain has played a key role in redefining supply chain management and financial services. It has also enabled secure digital transactions.

Meanwhile, AI has progressed from theoretical concepts to practical applications. Major strides in machine learning have enabled AI to process and analyze data at unprecedented speeds and accuracy. This has led to innovations in fields such as autonomous driving and personalized medicine. The integration of AI and blockchain technologies can create a transformative synergy.

AI can enhance the flexibility of smart contracts. Blockchain’s decentralized architecture can diversify AI data sources, reducing inadvertent biases in AI outputs. AI can also streamline and enhance blockchain’s scalability. It can detect and rectify anomalous behaviors, and potentially prevent hacks and other illegal activities.

However, the convergence of AI and Web3 also raises significant challenges. The methodologies these AI models employ have raised questions about data source reliability and information quality. The ownership of this access is also a concern. The journey of blockchain adoption is laden with challenges, including scalability issues and integration complexities.

Navigating the regulatory landscape is also a complex task. In light of these advancements and challenges, it is essential to recognize the need for meticulous regulatory and ethical considerations. The integration of AI and blockchain technologies has the potential to elevate transparency and reduce the role of intermediaries.

Unlocking the Potential of AI and Blockchain Convergence

However, ensuring the credibility of AI decisions and the accountability of blockchain transactions is necessary for operational excellence and public trust. In conclusion, the convergence of AI and Web3 represents a transformative step towards creating more ethical and effective technological solutions. By combining the strengths of each technology, we can address pressing challenges faced by digital innovations today.

These challenges include concerns around privacy, security, and ethical decision-making. AI brings unparalleled capabilities in data processing and pattern recognition. It drives efficiency and innovation across various sectors. However, its limitations, such as potential biases and lack of transparency, highlight the need for a complementary solution.

Web3 offers the perfect counterbalance to AI’s limitations. It ensures transparency, enhances data security, and empowers users with control over their information. Together, AI and Web3 are transforming industries and setting new standards for ethical technology development. Their integration fosters a more accountable and trustworthy digital ecosystem.

Decisions are not only data-driven but also socially responsible and aligned with ethical standards. The historical parallel of the Internet’s convergence with various technologies underscores the potential of this combination. The integration of AI and blockchain has the potential to bring about significant social transformations in the coming years and decades.

A Future of Sustainable Technological Advancements

However, global institutions and national governments must work together to mitigate fragmentation risks. They must ensure that AI model development inputs are complementary to human flourishing. The road to global coordination for blockchain standards and regulatory treatment is far more advanced. The same approach should be applied to AI.

As we look to the future, the synergy between AI and Web3 holds the key to unlocking sustainable technological advancements. Emerging trends such as decentralized finance and smart healthcare show promising applications. We encourage stakeholders in the AI and blockchain ecosystems to come together and leverage their collective expertise.

By working together, we can tackle the challenges and harness the opportunities discussed throughout the INATBA / EBA report. Ultimately, the integration of AI and blockchain encourages us to reimagine the possibilities of digital innovation. This journey towards integrating AI and blockchain propels us towards technological excellence and ensures that our advancements contribute positively to society and the environment.

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However, user friction has slowed Web3’s adoption. To accelerate adoption, we can integrate AI and blockchain technology. Consequently, this integration improves the user experience and reduces user friction. Additionally, decentralized AI built on blockchain can offer users personalized online experiences while protecting their privacy and control over their data.

Blockchain and AI are complementary technologies that address each other’s limitations. Specifically, blockchain’s built-in consensus protocol ensures data accuracy and integrity because it verifies data at multiple points. When AI is trained on flawed data, it produces flawed results. Therefore, we can design blockchain platforms to distribute power more evenly, reducing the risk of a few AI companies or models making opaque but consequential decisions. To leverage the power of AI and blockchain cohesively, we must overcome technical challenges. Next, we can track the co-development of these two technologies by examining their progression in three phases:

Data, Information, and Knowledge

Meanwhile, AI can accelerate Web3 adoption by offering personalized experiences based on user prompts. For Web3 to go mainstream, the next generation of consumer-facing applications must match the user experiences of Web2. To achieve this, we must prioritize personalization, which optimizes marketing spend. Decentralized knowledge graphs – as they are developed in the Artificial Superintelligence Alliance ASI – may be the key to bringing personalized experiences from Web2 into Web3. Knowledge graphs map relationships between objects, facts, events, situations, and other data. In addition, we can make knowledge graphs more accessible and verifiable by using decentralized knowledge graphs and open, permissionless blockchain networks. By integrating AI and Blockchain, we can create a foundation for Web3 built on trustworthy data. As a result, this new decentralized internet addresses issues prevalent in our current centralized internet.

AI and Blockchain Together Improve Internet Governance

• Decentralization: Web3 is built on blockchain technology, which is decentralized and transparent. We can use AI to ensure that data is stored and processed securely and transparently, reducing the risk of a single entity controlling or manipulating data. In this way, we can promote a more democratic and equitable Internet governance.
• Trust and Transparency: AI verifies the authenticity and accuracy of data on the blockchain, ensuring that information is trustworthy and transparent. With this information, users can make informed decisions and reduce the spread of misinformation. Moreover, AI can detect and prevent malicious behavior, ensuring that the internet remains a safe and trustworthy environment.
• Autonomy and Agency: Web3’s decentralized architecture and AI’s ability to automate decision-making processes empower users to take control of their personal data and online experiences. Users can then make choices that align with their values and preferences. Consequently, users have more autonomy and agency over their online experiences.
• Security and Privacy: AI detects and prevents cyber threats, ensuring that Web3 applications are secure and private. By doing so, AI protects users’ sensitive information and prevents unauthorized access or manipulation. Additionally, AI optimizes Web3 applications, enabling them to scale efficiently and process vast amounts of data in real time.
• Scalability and Efficiency: As a result of AI optimization, the internet’s overall performance and responsiveness improve. Furthermore, AI helps design and optimize token-based incentive systems that encourage desired behavior and promote a healthier internet ecosystem.
• Data-Driven Decision-Making: AI analyzes data generated by Web3 applications, providing insights that inform data-driven decision-making. With these insights, we can make decisions based on objective data rather than personal biases or assumptions. Therefore, we can make more informed decisions that benefit users.
• Accountability and Governance: AI ensures accountability and transparency in Web3’s decentralized governance models by detecting and preventing malicious behavior. As a result, the internet remains a safe and trustworthy environment.

In conclusion, by combining AI and Web3, we can create a more decentralized, transparent, and secure internet that prioritizes user autonomy, privacy, and agency. We can make decisions based on objective data and respond more effectively to the needs of users. Ultimately, this new decentralized internet can promote a healthier and more equitable online ecosystem.

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The demonstration involving Tesla and Jaguar electric vehicles will allow the visitors to experience the moveID’s vision of a vendor-neutral open ecosystem of data-driven mobility services with integrated payment capabilities. The live demo will show how autonomous transactions between connected devices streamline expenses and add new revenue streams.

In preparation for the big event, several moveID consortium members, namely BOSCH, DENSO, ITK Engineering,peaq, htw saar, Ocean, and 51Nodes,  have met for integration workshops in the Datarella offices in Munich. The goal of the first integration workshop was to connect the different software and hardware components provided by the partners and test the first set of workflows for the IAA demo from a user’s perspective. Datarella was entrusted with the planning and organization of the event as well as the integration of the different workflows into a seamless user experience via the MOBIX app.

The critical path that was completed, included three workflows, namely issuance of a Verifiable Credential to a vehicle, access to a parking space via a Verifiable Presentation to a Signal Head, and checkout of a parking space via a Verifiable Presentation to a Signal Head. The workflows are enabled by state-of-the-art Self-Sovereign Identity (SSI), Vehicle-to-Everything (V2X) as well as Fetch.aI micro-agent technology, all controlled via the MOBIX mobile application.

“Leveraging our long-time partner Fetch.ai’s micro-agents to connect different technologies and seeing the distilled result of the agent’s work in our MOBIX app is living proof of the power of the moveID tech stack,” says Datarella CEO Michael Reuter.

While the demonstration focuses on the mobility industry, the business model behind it can be implemented in a wide array of industries, such as Public-to-Private power grid interplay and smart supply chain management. The moveID project invites all stakeholders in the EV charging ecosystem to participate and make their charging solution part of the overall charging network. If you are interested in test drives, please contact our IAA Mobility team!

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As a lawyer by training with a focus on high-tech and innovation, Dr. Guerra previously worked in the private sector and is active as a board member in higher education and Medtech. She lectures and regularly publishes emerging tech and other innovation topics from a governmental perspective. With her unique insight into one of the most crypto-friendly markets in Europe, and her experience with regulation and policy work in the DeFi and blockchain, digitalization, and technology education field, she is a perfect fit for the association and its goal to empower the European blockchain ecosystem.

Adding a unique European viewpoint

“We could not be happier about Clara’s decision to join our board. She has a wealth of expertise and brings fresh ideas, insights, and points of view to the continuing conversations taking place within the blockchain and cryptocurrency ecosystem. In the current era of the industry, when nuanced and pragmatic dialogue is needed more than ever before, her skills in regulation and policy will be extremely beneficial. We are looking forward to working together with the other members of the EBA board to advance our mission of enhancing and bolstering the capabilities of the European blockchain ecosystem”, says Dr. Michael Gebert, Chairman of the European Blockchain Association.

“With its semi-autonomous, decentralized operating model, the European Blockchain Association is quite different from other associations. Our goal is to provide a platform for people who want to make an impact.  Clara joining our board explicitly because she shares our vision and is willing to actively contribute to our various projects and working groups is a big win for the EBA and its members. We are thrilled to be working with her”, Datarella CEO Michael Reuter, Co-Chair of the European Blockchain Association, adds.

Policy and regulation – an EBA strong suit

After three years of building a base for the association, establishing working groups from Proof of Stake to SSI, Custody, Education, Tokenization, and NFT, and giving its members a voice in relevant industry discussions, in 2023 the European Blockchain Association will add a focus on regulatory and policy work. With the appointment of Erwin Voloder as Senior Policy Fellow last year and Dr. Clara Guerra as a new board member, the association is best equipped to contribute strong expertise to public discussions, regulatory bodies, and across European projects.

“Fostering innovation has always been my passion. Innovation is conventionally associated with strength. However, most of the time it is a rather fragile transition state that relies on enabling circumstances. Regulation is a key enabler for technological innovation, especially in the finance sector, and must be approached with deliberate consideration. I am very excited to join the European Blockchain Association. The active and very knowledgeable community, the pragmatic mindset, and the decentralized approach to achieving its goals are perfect frameworks for me. I am looking forward to collaborating with my fellow board members and the whole EBA community”, says Dr. Clara Guerra.

Kent Gärtner, who was a board member of the EBA since its inception is stepping down from his position for personal reasons. The board would like to thank him for his invaluable contributions in the start-up phase of the association.

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Moderated by John Hildebrandt, our CEO Michael Reuter, and our CTO Rebecca L. Johnson will explain the role and goal of Datarella in moveID, the €20M+ Gaia-X project.  This AMA will be hosted as a Twitter Space on the moveID Twitter account. If you want to learn more about moveID, Gaia-X, and how it feels to work in this consortium consisting of 19 large corporations, SMEs, Web3 projects, and research institutes, tune in!

If you already have questions, feel free to add them in the comment section. We’ll try to pick them up and answer during the Live session! Feel also free to follow us on Twitter, and LinkedIn and discuss all things Web3 with the Datarella team – see you there!

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