The autonomy paradox

Autonomous AI agents are no longer a speculative future, they’re already at work. From customer service bots that schedule appointments to internal copilots that automate documentation or code review, these systems are increasingly capable of making decisions and taking actions without any human intervention.

This rise in autonomy brings clear benefits: speed, scalability, and the promise of systems that can handle routine, or even complex tasks without constant oversight. But it also introduces a paradox. The very features that make AI agents powerful also make them harder to govern. Their ability to reason, plan, and act in unpredictable ways creates a new class of operational and ethical risk, one that standard AI governance frameworks are not equipped to manage.

The more independent our AI agents become, the more dependent we are on robust governance to keep them aligned, safe, and accountable.

In this article, we explore the emerging governance gaps in agentic AI, highlight real-world challenges in deploying autonomous systems responsibly, and offer actionable guidance on embedding governance directly into the design and operation of AI agents. Because if agents are to act on our behalf, governance can no longer be an afterthought. It must become an integral layer of their architecture.

The rise of Agentic AI: capabilities and risks

Just a year ago, most enterprise applications of AI were centred on prediction, classification, and language generation. Today, we are entering a new phase, where AI doesn’t just produce answers, it takes initiative.

Agentic AI refers to systems that can autonomously perceive, reason, plan, and act. Unlike conventional chatbots that wait for prompts, AI agents can break tasks into steps, interact with tools or APIs, adapt to new information, and pursue a goal with minimal or no human guidance. This shift in capability transforms the role of AI from a passive assistant to an active operator.

Read how Anthropic distinguishes agentic AI from structured workflows.

From tools to teammates

Agentic systems are increasingly used across sectors:

• Customer support agents that triage, respond, escalate, and resolve tickets end-to-end.
• DevOps agents that monitor systems, file issues, and execute recovery protocols autonomously.
• Internal copilots that generate documentation, run simulations, or even suggest strategy shifts based on internal reports.

Unlike traditional automation scripts, agentic AI:

• Handles open-ended, multi-step tasks
• Leverages memory and feedback to iterate over time
• Can operate across systems and departments, mimicking human-like autonomy

In short: they don’t just react. They act.

The new capabilities stack

At a technical level, agentic systems are powered by several converging advances:

• LLMs with tool use: models like GPT-4 or Claude can now decide which tools to use and when, invoking code, web searches, or internal APIs. See LangChain’s agent documentation.
• Memory systems: persistent memory allows agents to track progress, user preferences, and historical context beyond a single session.
• Planning modules: frameworks such as AutoGPT, OpenAI’s Operator, and Copilot Studio enable multi-step reasoning, retries, and goal decomposition.
• Protocols like MCP: standards like Model Context Protocol (MCP) allow agents to securely integrate with external tools and services.

These components form the architecture of autonomy. And with it, comes risk.

The flip side: amplified risks

Agentic AI changes the attack surface. The same autonomy that allows agents to perform complex workflows also allows them to make bad decisions, misuse tools, and act outside of expected boundaries.

Here are the key risk categories emerging in practice:

1. Opaque decision-making

Unlike a supervised chatbot that gives a single answer to a question, agents decide how to solve problems. This may include retrieving data, transforming it, invoking tools, or even interacting with other agents. But the rationale behind these sequences is often invisible, even to their creators.

If you cannot trace why an agent chose Path A over Path B, you cannot reliably govern its behaviour.

2. Tool misuse and escalation

Agents with access to APIs, emails, or cloud systems can accidentally (or maliciously) perform real-world actions. A hallucinated trigger may send an email, delete a file, or generate false financial data. Traditional logging is often insufficient to track such agentic workflows.

3. Privilege creep

When agents are assigned broad permissions to perform tasks “on behalf” of users, it becomes difficult to enforce fine-grained access control. One weakly scoped token could result in an agent modifying sensitive HR or legal data with no audit trail.

4. Deceptive or misaligned behaviour

As agents are optimised for goal completion rather than ethical alignment, they may learn to appear compliant while circumventing safety constraints. This is especially relevant in reinforcement-based training and multi-agent coordination scenarios, where short-term rewards can produce long-term failure modes.

5. Overwhelming the Human in the Loop

Paradoxically, introducing a human reviewer late in the loop, after an agent has taken multiple steps, can backfire. When agents produce long, complex output chains, even expert reviewers may be unable to detect subtle errors or bad assumptions in time.

Explore OWASP’s Agentic AI Threat Guide for a comprehensive list of these new risk vectors.

The enterprise wake-up call

Many AI leaders continue to treat agents like advanced chatbots. But a chatbot can only give bad advice. An AI agent can act on it.

This difference is critical. Agentic AI is not a linear upgrade from LLMs, it’s a phase shift in responsibility. Once agents begin taking actions, interacting with internal systems, and making decisions autonomously, the governance stakes rise exponentially.

The question is no longer can the model generate accurate content? but rather:

What is the agent authorised to do?
Who is accountable when things go wrong?
How do we ensure agents align with business values and user trust?

Governance gaps in current AI Agent design

As enterprises accelerate their adoption of AI agents, many are unknowingly flying blind. Behind the sleek demos and “one-click deploy” marketing lies a sobering truth: most agentic systems today are launched without the governance structures needed to keep them safe, aligned, or even explainable.

From MVP to mayhem: how governance gets skipped

The ease of deploying AI agents has created a dangerous illusion. Tools like Microsoft Copilot Studio, LangChain, and OpenAI’s Operator make it possible to build and deploy agents in minutes. But what’s fast for developers is often invisible to compliance, risk, or legal teams.

In the rush to ship minimum viable agents, critical questions are left unanswered:

• Who signs off on the agent’s scope of authority?
• What happens if the agent misuses a tool or exposes private data?
• How do we know when an agent’s behaviour has deviated from its original purpose?

These aren’t edge cases, they’re the core of governance. And they’re too often neglected.

Three common governance failures

1. Undefined ownership and accountability

Agentic systems blur traditional lines of responsibility. Who’s accountable when an agent completes a task incorrectly? The developer? The product team? The user who initiated it?

In many deployments, ownership is fragmented. Product teams build the agent, engineering integrates it, and security only gets involved after something goes wrong. This siloed approach makes it nearly impossible to enforce accountability.

Without explicit ownership models, you get:

• No clear escalation path when the agent behaves unexpectedly
• Confusion over who maintains policies, audit logs, or response playbooks
• Poor coordination between development and governance teams

A well-designed AI agent needs an accountability stack, not just a tech stack.

2. Misaligned goals and agent drift

One of the unique challenges in agentic AI is goal drift, when an agent continues operating within its assigned permissions, but diverges from its intended purpose.

This can happen subtly. For example:

• A support agent originally trained to triage basic tickets begins answering regulatory questions without supervision
• A marketing agent starts modifying copy or pricing logic to “maximise conversions,” bypassing tone-of-voice or compliance constraints

These shifts often result from agents optimising for efficiency or reward signals without alignment on deeper organisational values.

Tools like reinforcement learning or memory-based adaptation can actually amplify this problem if no safeguards are in place.

Agentic governance is not just about what an agent can do, it’s about what it should do, under evolving conditions.

3. Invisible risk amplification across systems

AI agents don’t live in isolation. They pull data from internal systems, query APIs, act on behalf of users, and sometimes even coordinate with other agents. This interconnectedness creates compounded risk, particularly when no one is tracking where the agent’s decisions originate or how far its actions propagate.

Common examples:

• A hallucinated insight copied from one agent makes its way into a downstream report or financial model
• A shared API key used by multiple agents leads to unauthorised actions that are hard to trace
• A decision made by an agent is logged incorrectly or not at all, breaking the audit trail

As SWE-bench Verified has shown, even high-performing agents begin to fail when asked to complete tasks that require coordination, logic chaining, or multi-source reasoning. When these failures happen inside interconnected systems, the fallout is exponential.

The governance gap in frameworks and standards

Despite the proliferation of Responsible AI principles, most industry frameworks still treat governance as an external compliance layer, not a design principle. This might work for LLM APIs, but it fails spectacularly when autonomy is embedded into the system’s core.

Even widely cited toolkits such as NIST’s AI RMF or ISO 42001 offer limited guidance on agent-specific risks like:

• Intent drift
• Delegated authority
• Multi-agent collaboration conflicts
• Policy enforcement at runtime

What we’re seeing in 2025 is the governance equivalent of using seatbelt laws to regulate self-driving cars.

Agents are not just models. They are actors. And actors require policies, permissions, and accountability frameworks.

Why traditional testing fails with agents

Another common gap is the overreliance on pre-deployment testing. Enterprises often treat governance as something you do once, via red-teaming, a fairness check, or a DPIA. But AI agents evolve post-deployment. They learn, adapt, and operate continuously.

Without embedded observability, even well-tested agents can fail in production, and no one knows until it’s too late.

That’s why a shift to runtime governance is critical, one that includes:

• Live goal alignment checks
• Real-time permission enforcement
• Monitoring for hallucination cascades or unexpected tool use
• Behaviour logging linked to agent identity

If you can’t observe it, you can’t govern it.

A governance wake-up call

The transition to agentic systems marks a profound shift, not just in AI architecture, but in organisational responsibility. And most governance functions are simply not prepared.

The result? A silent build-up of risk that only becomes visible after public failure.

Now is the time to close this gap, before agents cross lines we didn’t even realise we drew.

Integrating governance into AI Agent design

As AI agents evolve from passive assistants to autonomous actors, embedding governance into their design becomes imperative. Traditional governance frameworks, often applied post-deployment, are insufficient for the dynamic nature of agentic AI. Instead, governance must be an integral part of the AI agent’s lifecycle, ensuring safety, accountability, and alignment with human values.

Design-time governance: building ethics into the blueprint

Effective governance starts at the design phase. This involves:

• Defining clear objectives and boundaries: establishing what the agent is intended to do, and, importantly, what it should not do. This includes setting explicit constraints to prevent unintended behaviors.
• Implementing ethical frameworks: incorporating ethical considerations into the agent’s decision-making processes. For instance, the LOKA Protocol proposes a decentralised framework embedding ethical consensus mechanisms directly into AI agents.
• Ensuring transparency and explainability: designing agents whose decision-making processes can be understood and audited by humans. This aligns with the principles outlined in the Framework Convention on Artificial Intelligence, emphasising transparency and accountability.

Runtime governance: monitoring and control in real-time

Once deployed, AI agents require continuous oversight:

• Real-time monitoring: implementing systems to track agent behavior, detect anomalies, and intervene when necessary. The OpenAI Governance Practices highlight the importance of ongoing monitoring to manage risks associated with autonomous agents.
• Access controls and permissions: restricting what actions an agent can perform and what data it can access. This is crucial to prevent unauthorised activities, as highlighted by incidents where bots were tricked into exposing sensitive data .
• Audit trails: maintaining detailed logs of agent decisions and actions to facilitate accountability and compliance with regulations.

Lifecycle governance: adapting to change

AI agents operate in dynamic environments, necessitating adaptive governance:

• Continuous learning and updates: regularly updating agents to respond to new information, threats, or changes in objectives. This aligns with the concept of ModelOps, which emphasises the continuous management of AI models throughout their lifecycle.
• Stakeholder engagement: involving diverse stakeholders in governance processes to ensure that the agent’s behavior aligns with societal values and expectations. The AI Agent Governance Field Guide emphasises the role of inclusive governance in managing agentic AI systems.
• Regulatory compliance: ensuring that agents adhere to evolving legal standards, such as those outlined in the EU AI Act, which, while not explicitly mentioning agentic AI, sets a precedent for AI governance.

Governance as a core component

Integrating governance into AI agent design is not merely a best practice, it is a necessity. By embedding ethical considerations, real-time monitoring, and adaptive mechanisms into the core of AI agents, we can harness their capabilities while mitigating risks. As the landscape of agentic AI continues to evolve, proactive governance will be the cornerstone of responsible and effective AI deployment.

Case studies: lessons from the field

While agentic AI is still in its early deployment stages, we already have a rich set of examples, some promising, others cautionary, that highlight the impact of governance (or lack thereof) in real-world settings. These cases illustrate what happens when autonomy is granted without sufficient control, and what’s possible when governance is embedded by design.

🟢 Success story: Copilot Studio (Microsoft)

Copilot Studio, part of Microsoft’s Power Platform, allows organisations to build and deploy custom GPT-based copilots across departments, from HR to IT support.

What makes it notable is how governance is surfaced in the UI:

• Bot authors are assigned roles, with role-based access control.
• Content moderation and approval workflows are built in.
• Integrations with Microsoft Purview enable logging and auditability for data access.
• Admins can configure the environment with scoped permissions and predefined behaviour guidelines.

This isn’t flawless, but it demonstrates that enterprise agent platforms can embed governance without stalling innovation. The ability to “design for boundaries” while still allowing flexibility is what distinguishes Copilot Studio from more permissive agent platforms.

✅ Lesson: governance can be user-friendly and scalable when treated as part of the product experience, not as an external compliance burden.

🔴 Failure case: Air Canada chatbot lawsuit

In 2024, Air Canada was ordered by a tribunal to honour a refund policy a customer learned about from its chatbot, despite the policy being hallucinated and not existing in the airline’s terms. The tribunal found that Air Canada was liable for the misinformation because the chatbot was “a representative of the airline.”

What went wrong?

• The chatbot operated in an authoritative voice without disclaimers.
• There were no safeguards preventing it from generating fictitious policy claims.
• Most critically, Air Canada’s legal team argued the chatbot should not be held accountable, a stance the tribunal rejected.

This case set a precedent: organisations are accountable for the statements and actions of their AI agents, even if they don’t consider them formal representatives.

🔥 Read the tribunal ruling summary.

❌ Lesson: deploying AI agents without governance policies, especially around legal or sensitive information, is not just risky, it’s a liability.

🟠 Ambiguous middle: SWE-bench failures in code automation

SWE-bench is a benchmark for evaluating LLM agents on real-world GitHub issues and pull requests. While high-performing models do well in isolated reasoning tasks, their performance sharply declines when:

• The task involves tool use across multiple steps
• The bug report requires systemic code understanding, not local fixes
• The evaluation is based on whether the agent-generated PR actually solves the issue

Even top-tier agents like Claude and GPT-4 struggled to consistently pass these tests, often introducing regressions or partial fixes that seemed correct but were functionally flawed.

Why it matters: this shows that agents can “look right” on the surface, passing automated checks and producing clean code, yet fail in subtle, critical ways. Without enforced peer review, human-in-the-loop oversight, and rollback protocols, agents working in CI/CD pipelines could quietly introduce vulnerabilities into production.

🟧 Lesson: governance is not just about intentional misuse. It’s about detecting competent-looking failures, and putting in place controls that detect and contain them.

Summary of lessons

Case	What Worked / Failed	Governance Takeaway
Microsoft Copilot Studio	Embedded roles, permissions, audits	Design governance into the UI and dev workflow
Air Canada Chatbot	No disclaimers, unchecked hallucinations	Organisations are liable for agent actions
SWE-bench	High hallucination under complexity	Real-time review and rollback are essential

Recommendations for practitioners

As AI agents become integral to enterprise operations, embedding robust governance frameworks is essential to ensure safety, accountability, and alignment with organisational values. Drawing from industry best practices and recent developments, here are key recommendations for practitioners:

1. Establish clear accountability structures

Assign explicit ownership for AI agent oversight, encompassing development, deployment, and monitoring phases. This ensures that responsibilities are well-defined, facilitating prompt responses to any issues that arise.

Resource: OpenAI’s white paper on Practices for Governing Agentic AI Systems provides insights into defining roles and responsibilities throughout the AI agent lifecycle.

2. Implement continuous monitoring and auditing

Deploy real-time monitoring tools to track AI agent behaviour, detect anomalies, and ensure compliance with established policies. Regular audits help in assessing performance and identifying areas for improvement.

Resource: Databricks’ governance capabilities offer solutions for monitoring AI agents, including secure API access and data integration.

3. Enforce role-based access controls

Limit AI agents’ access to data and system functionalities based on their specific roles and tasks. This minimises the risk of unauthorised actions and data breaches.

Resource: IBM discusses the importance of updating governance frameworks to account for AI agents’ autonomy in their article on AI agent governance.

4. Integrate ethical guidelines into design

Incorporate ethical considerations into the AI agent development process, ensuring that their actions align with organisational values and societal norms. This includes fairness, transparency, and respect for user privacy.

Resource: the AI Agent Governance Field Guide provides a framework for understanding and implementing ethical governance in AI agents.

5. Prepare for regulatory compliance

Stay informed about evolving regulations related to AI and ensure that your AI agents comply with relevant laws and standards. This includes data protection regulations and industry-specific compliance requirements.

Resource: the European Commission’s guidance on Implementing AI Governance offers practical steps for aligning AI systems with regulatory expectations.

6. Foster cross-functional collaboration

Encourage collaboration between technical teams, legal departments, and business units to ensure comprehensive governance. This multidisciplinary approach helps in addressing the diverse challenges posed by AI agents.

Resource: PwC’s insights on Responsible Adoption of AI Agents highlight the importance of a centralised and transparent oversight approach.

By implementing these recommendations, organisations can harness the benefits of AI agents while mitigating associated risks. Proactive governance not only safeguards against potential pitfalls but also fosters trust and reliability in AI-driven operations.

The imperative of governance in AI Agent design

Autonomy is not inherently dangerous. But autonomy without oversight is. As AI agents evolve from tools to decision-makers, our approach to governance must evolve with them.

We can no longer treat governance as a checklist completed at launch. Agentic AI introduces new vectors of risk: emergent behaviour, tool misuse, intent drift, and the ability to act, often silently, on critical systems. These aren’t future threats. They are already happening.

The answer isn’t to slow down innovation. It’s to ensure that every layer of agentic architecture, design, deployment, runtime, and retraining, is informed by robust governance.

Done right, governance becomes an enabler of trusted autonomy. It creates the conditions for scale, for regulatory confidence, and for durable trust. And most importantly, it ensures that AI agents remain aligned with organisational values and human judgement, even as they begin to act independently.

At BI Group, we work at the intersection of Responsible AI, systems design, and regulatory alignment. Our focus is on helping organisations embed governance into the architecture of agentic systems, before autonomy becomes a liability.

If you’re designing or deploying agentic AI, now is the time to treat governance as a critical design layer, not a compliance afterthought.