Why agentic AI projects fail: 10 Learnings and fixes (for those already past the co-pilot phase)
Written by: Jonas Röst, Simon Althoff, and Jens Eriksvik
For organizations no longer tinkering with copilots and chat overlays, this is what breaks when you scale real AI colleagues, and how to fix it.
Many organizations have outgrown their Gen AI phase. The chatbots have been demoed. The copilots deployed. The value delivered was questionable, at best. Now the ambition is bigger: autonomous AI agents that can work alongside humans; taking action, owning tasks, and evolving as part of the team. But somewhere between the pilot and production, things fall apart.
This article is for teams already past the novelty curve. It draws on lessons from real-world agent deployments, and the structural, design, and governance failures that quietly derail them. These are systemic problems. If you want your digital colleagues to succeed, you’ll need to treat them less like tools, and more like teammates.
The co-pilot “era” taught us how to prompt models. But prompting isn’t delegation, and outputs aren’t outcomes. Co-pilots assist, they don’t own. Responsibility stays with the human.
Agentic AI shifts that. An agent stops being a feature and starts being a colleague, with real accountability. As outlined in When the Agent Takes Over [1], this is the key maturity threshold: not how smart the model is, but how much work it owns. That shift breaks more than it unlocks, at first.
Workflows aren’t built for handoffs, teams aren’t structured for digital peers, and governance isn’t ready for non-human accountability. These are organizational short-comings.
“At Algorithma, our most valuable learning is that the performance of autonomous AI agents depends less on the underlying model than on the systems and organizations that surround it. When agents are embedded with clear responsibilities, integrated into real workflows, and governed with the same discipline as human roles, we are able to create a solid foundation for success. In our experience, success comes not from smarter prompts, but from treating agents as operational peers.”
- Jens Eriksvik
This article is about what fails when you scale agentic AI, and how to fix it. It draws on market experiences and Algorithma’s recent projects in this space, where some of our clients have pushed through the hype to get real value.
The failure rate of Agentic AI, what the data tells us
Agentic AI, autonomous or semi-autonomous systems capable of executing tasks with limited human oversight, is still in its early operational maturity. While excitement around large language models (LLMs) has accelerated experimentation, most enterprise-scale deployments either stall or underperform, especially when transitioning from tool-based copilots to integrated digital colleagues.
Gartner warns that more than 40 percent of agentic AI projects could be cancelled by 2027 [2]
Between 60%–80% of enterprise AI projects fail to deliver expected outcomes, according to long-standing industry analyses. McKinsey’s 2024 report shows that while 72% of companies have embedded AI in at least one business function, only less than 20% have captured significant financial impact at scale [3]. These figures hold steady even in more mature sectors like financial services.
As we discussed previously [4], hallucination rates in LLMs remain a significant barrier to reliable agent performance. OpenAI's internal tests have shown hallucination rates up to 79% in reasoning tasks for their latest models, more than double earlier versions. In real-world deployments, hallucinations account for over one-third of all documented failures in enterprise LLM systems.
According to BCG, a significant 74% of companies struggle to achieve and scale value from their AI investments. The primary obstacles are not technological but are rooted in organizational and process-related issues. Specifically, 70% of these challenges stem from people and process factors, such as inadequate change management, lack of workflow integration, and insufficient AI talent and governance. In contrast, only 20% are attributed to technological shortcomings, and a mere 10% relate to the AI algorithms themselves. [5]
The data is clear, what breaks isn’t necessarily the tech, but the systems and organizations around it. From vague ownership structures to brittle workflows and governance gaps, failure stems from treating agents like smarter tools instead of operational colleagues. These numbers are a warning for those scaling past prototypes. We see four main categories of failures: 1) Design failures (Scope, responsibility, and boundaries), 2) Deployment failures (Integration, execution, and grounding), 3) Governance failures (Accountability, observability, and risk control), and, 4) Iteration failures (Learning, supervision, and growth).
“An agent without built-in memory, robust interfaces, and enforced escalation logic isn’t an autonomous actor, it’s a stateless inference loop masquerading as one. What fails isn’t the model, but the absence of an operational layer.”
- Simon Althoff
Design failures
Design is where most agentic AI projects are set up to fail - often before they even launch. The root cause is often poorly defined problem boundaries and unclear task breakdowns. Large language models (LLM) are designed to always generate an output for any given input, regardless of how ill-defined or conflicting the instructions may be - they will fill in the blanks and risk hallucination if not given clear directions.
1. The agent doesn’t have a job description
Many organizations approach agentic AI by simply giving a LLM a sophisticated prompt and expecting it to self-organize and execute complex tasks [6]. This neglects the fundamental need for structured task ownership, clear delegation, and defined boundaries. Without a "job description" outlining its purpose, scope, and interaction points, an agent operates in a vacuum, often generating plausible but unhelpful or even erroneous outputs because it lacks a clear understanding of its organizational role and responsibilities. It's like hiring an employee without telling them what they're supposed to do.
The fix: Design agent roles like team roles - scope tasks, escalation logic, and review points.
Define clear job descriptions: For each agent, articulate its primary function, specific responsibilities, and expected outcomes. What problem is this agent solving? What specific part of a larger process does it own?
Establish boundaries and context: Clearly define the agent's operational environment and the data it can access or influence. Specify what it should and should not do.
Implement escalation paths: Determine when and how an agent should escalate an issue to a human, another agent, or a predefined system. This prevents the agent from getting stuck or making critical errors it's not equipped to handle.
Set review points and metrics: Just like human performance reviews, establish clear criteria for evaluating an agent's success and identify regular checkpoints for human oversight and intervention.
At Algorithma, we have seen this firsthand. At Byggmax, building a customer service agent modeled after a human colleague was imperative in both creating better customer service, but also in humanizing the hand-offs between the digital and the human colleagues [see more on the impact of AI on human jobs, 7].
2. The workflow wasn’t built for teammates, it was built for tools.
Existing enterprise workflows are typically designed for human-to-system interactions (e.g., a human user interacting with a CRM, an ERP, or a ticketing system) or simple, sequential automation (e.g., RPA bots performing rote tasks). They lack the flexibility, communication protocols, and ambiguity tolerance required for an AI "teammate" that might generate partial results, ask clarifying questions, or require iterative feedback. Trying to force an agent into a rigid, linear human-tool workflow leads to bottlenecks, errors, and significant rework.
The fix: Redesign workflows to include coordination, not just delegation, support mid-task handovers and fallback paths.
Map human-AI touchpoints: Identify precisely where humans and AI agents will interact, hand off tasks, and collaborate. This goes beyond simple delegation to active co-execution.
Enable iterative loops: Design workflows that account for an agent's ability to provide intermediate outputs, receive human feedback, and refine its work. This requires flexible process orchestration.
Build in "Ask for Help" mechanisms: Create clear paths for agents to request clarification, additional data, or human intervention when faced with uncertainty or unexpected scenarios.
Develop fallback strategies: What happens if the agent fails or cannot complete a task? Establish automated or human-led fallback procedures to ensure continuity and prevent system halts.
Deployment failures
This area is a common failure, where the core foundational LLM models lack the ability to distinguish between plan and action. Clear safeguards need to be built into the agent, based on a thought-through architecture.
3. The agent can’t act, it can only simulate.
Many early agentic AI pilots focus on the AI's ability to reason and plan actions based on prompts, but fail to provide the actual means for the agent to execute those actions in real enterprise systems. The agent might "say" it processed a refund or updated a record, but without genuine API integrations or robust connectors to systems of record, these are mere simulations, leading to a "ghost in the machine" effect where the AI seems capable but has no real-world impact [8].
The fix: Move from fake actions (“Refund processed”) to real integrations via secure API calls and confirmations.
Deep system integration: Provide agents with secure, authenticated access to the necessary enterprise systems (CRM, ERP, ticketing, financial systems, etc.) via well-defined APIs.
Robust tooling and orchestration: Equip agents with the "tools" (API wrappers, specialized functions) they need to interact with these systems and orchestrate multi-step actions across different platforms. Providing tailored tools for each agent ensures that any retrieved information and performed actions are relevant only to the agent’s tasks.
Transaction confirmation: Implement mechanisms for agents to receive explicit confirmation that an action was successfully executed by the target system, rather than assuming success. Additionally, transaction failures can be utilized to escalate a given task to a human colleague.
Security and permissions: Define and enforce access controls for agents, ensuring they only have the minimum necessary permissions to perform their designated tasks. Again, tailored tools for each agent and task provides a framework for incorporating limited permissions into an agent’s workflow.
4. The agent guesses when it should ask.
A common challenge with LLM-based agents is their tendency to hallucinate when they lack sufficient context or are uncertain. Without connecting their knowledge to verifiable data and explicit refusal logic, agents might confidently provide incorrect answers or attempt inappropriate actions, leading to errors, distrust, and potentially serious business consequences. They lack the human intuition to know when they don't know.
The fix: Use RAG, uncertainty thresholds, and built-in escalation to prevent hallucinated actions.
Retrieval augmented generation (RAG): Ground the agent's knowledge in reliable, internal data sources (databases, knowledge bases, documents) using RAG architectures. This ensures responses are based on verifiable facts, not just probabilistic guesses. It also allows for analysing where the information came from in the event of a failure, improving the possibility of root cause analysis.
Uncertainty thresholds: Implement mechanisms that allow the agent to assess its own confidence level in a response or action. If the confidence falls below a defined threshold, it should be programmed to escalate or refuse to act. Clearly defining the behaviour in a system prompt can help in some cases, but adding more complex mechanisms, such as an uncertainty quantification layer, can prove a much more robust solution.
Explicit refusal logic: Train agents to explicitly state "I don't know" or "I cannot perform that action" when they lack the necessary information, authorization, or certainty.
Clear escalation protocols: For cases of uncertainty or inability to act, define the precise steps for the agent to escalate to a human or another designated system.
5. The agent works in isolation, like a consultant without context.
While individual agents may perform well on discrete tasks, real enterprise work is collaborative and context-dependent. If agents lack shared memory, real-time access to the current state of a process, or awareness of broader organizational policies, they act like disconnected contractors. They might duplicate efforts, miss context from previous interactions, or violate unstated rules, leading to inefficiencies and errors.
The fix: Embed agents into shared task state, policy layers, and real-time system context.
Shared memory and context: Implement shared memory stores or context layers that allow agents to retain information across interactions and understand the ongoing state of a task or project. This could involve persistent session data, knowledge graphs, or shared workspace environments.
Policy-as-code integration: Integrate enterprise policies, compliance rules, and best practices directly into the agent's operational logic as machine-readable rules (see point 8). This can be integrated in the tools that the agent uses to interact with the surrounding systems. And it can be reinforced in prompts to the model itself, potentially served dynamically based on the current task.
Real-time system awareness: Provide agents with real-time feeds from relevant systems (e.g., status updates, inventory levels, customer profiles) to ensure they always operate with the most current information.
Agent orchestration: Design systems where multiple agents can coordinate and share information to complete complex, multi-agent workflows, mimicking human team collaboration.
Governance failures
Once again, an LLM based AI agent will generate an output for any input - no matter the instructions it is given. Because the models are fundamentally probabilistic, we can’t rely on our agents to follow our directions perfectly every time without oversight. The only way to ensure our digital colleagues act as intended is through robust oversight: establishing monitoring and controls that go beyond just instructions.
6. Nobody knows who’s responsible when it fails.
When an agent takes autonomous action and something goes wrong (e.g., an incorrect order is placed, a customer is given wrong information), the lack of a clear accountability framework leads to confusion, blame, and difficulty in root cause analysis. Traditional organizational structures aren't designed for "digital colleagues," and without defined ownership, remediation becomes chaotic.
The fix: Implement RACI models and span-of-responsibility diagrams for agents, just like for humans [9].
RACI for Agents: Assign "Responsible," "Accountable," "Consulted," and "Informed" roles not just to human teams but also to specific AI agents for particular tasks and outcomes. This clarifies who owns the output, who oversees the agent, and who needs to be aware.
Span-of-Responsibility diagrams: Visually map out the areas an agent is responsible for, its dependencies, and its interactions with other agents and human teams.
Defined human oversight: Identify the specific human individuals or teams who are ultimately accountable for the agent's performance and decision-making, especially in critical or high-risk scenarios, leverage HITL and HOTL.
Legal and ethical accountability: Work with legal and compliance teams to establish frameworks for liability and ethical responsibility when AI agents operate autonomously.
7. The system can’t tell when the agent is wrong.
Without robust monitoring and observability, an agent could be making subtle errors or underperforming without anyone realizing it until a significant problem arises. Relying solely on human complaints or manual checks is unsustainable at scale. Lack of telemetry means there's no data to understand agent behavior, identify performance drifts, or pinpoint the source of failures.
The fix: Track task completion, rework rate, escalation frequency, and decision confidence per agent [11].
Comprehensive logging: Log all agent inputs, outputs, internal reasoning steps, tool calls, and system interactions.
Performance metrics: Define and continuously track key performance indicators (KPIs) for each agent, such as:
Task completion rate: Percentage of assigned tasks successfully completed end-to-end.
Rework rate: Frequency with which a human has to correct or redo an agent's work.
Escalation frequency: How often the agent requests human intervention.
Decision confidence: Metrics reflecting the agent's internal certainty (if applicable).
Error rates: Specific types of errors made by the agent.
Alerting and anomaly detection: Implement automated alerts for significant deviations from expected performance or sudden increases in error rates by adding thresholds to tracked KPIs, or implementing anomaly detection models.
Traceability: Ensure that every action taken by an agent can be traced back to its originating prompt, data inputs, and internal decision-making process.
At several of our clients we have also built “oversight” and “evaluation” agents that in run-time oversee every decision and interaction, creating observability, transparency and the basis for escalation decisions.
8. The agent bypasses guardrails, or creates new risks.
Traditional security and compliance mechanisms might not adequately cover the dynamic, generative, and autonomous nature of agentic AI. Hard-coded rules can fail in dynamic environments and prove difficult to scale, and agents, by their nature, can find novel ways to accomplish goals that might unintentionally circumvent existing guardrails or introduce new, unforeseen risks (e.g., data leakage, unauthorized access, compliance violations).
The fix: Translate business policies into machine-readable rules, and embed them at runtime [10].
Policy-as-code: Convert all relevant business rules, compliance policies, security protocols, and ethical guidelines into machine-executable code. This ensures consistent application across all agent actions.
Runtime policy enforcement: Embed these policies directly into the agent's execution environment, so they are checked and enforced before any action is taken.
Proactive risk assessment: Continuously assess potential new risks introduced by agent autonomy, especially concerning data privacy, security, and ethical implications.
Auditability: Ensure that all policy checks and enforcement decisions are logged and auditable, providing a clear record of compliance.
Iteration failures
AI agents don’t improve on their own. Unlike human colleagues, they won’t learn or adapt unless continuous improvement is built into their lifecycle. Without structured feedback, evaluation, and a plan for maturity, agents remain static and organizations end up with a sprawl of underperforming tools. Real value comes from treating agents as evolving team members: investing in their development, measuring their impact, and expanding their capabilities over time.
9. The agent never gets better.
Unlike human employees who learn from experience and feedback, AI agents often remain static after initial deployment if there's no structured mechanism for continuous improvement. Without a feedback loop that ties their performance to model retraining or rule updates, agents will repeat errors, fail to adapt to changing conditions, and miss opportunities to become more effective.
The fix: Build feedback into post-task review, tie agent outputs to result quality and retraining pipelines [12].
Structured feedback mechanisms: Design user interfaces and workflows that allow human supervisors to easily provide explicit feedback on agent outputs (e.g., "correct," "incorrect," "needs refinement"). In cases of text responses, consider allowing more nuanced feedback, a text response is seldom just “good” or “bad”.
Outcome-based evaluation: Don't just evaluate the agent's direct output, but its impact on the desired business outcome. Did the agent successfully resolve the customer issue, not just generate a response?
Retraining pipelines: Establish automated or semi-automated pipelines to collect feedback data, curate it, and use it to retrain or fine-tune the agent's underlying models (LLMs, decision models, etc.). A structured way of evaluating model performance is critical to this, such as establishing a metric that predicts outcomes as closely as possible.
A/B testing and experimentation: Continuously experiment with different agent configurations, prompt strategies, or model versions to identify improvements.
10. The org keeps launching new agents instead of maturing existing ones.
Driven by the "shiny new object" syndrome, hype cycles, or a fragmented approach to AI adoption, organizations often prioritize launching a multitude of new, often shallow, AI agents rather than investing in the deep maturity and expanded capabilities of existing ones. This leads to a sprawl of limited, disconnected AI tools that don't deliver cumulative value and create more management overhead.
The fix: Focus on expanding the span of responsibility as the real maturity metric, not model size or chat UX. Start small, promote when ready.
Strategic roadmapping: Develop a clear roadmap for agentic AI that prioritizes the deep integration and expansion of existing agents' capabilities and scope over simply adding more agents.
Maturity metrics: Define agent maturity not by technological novelty, but by its increasing ability to handle more complex tasks, operate with greater autonomy, reduce human intervention, and contribute more significant business value.
Capability expansion: Instead of building a new agent for a slightly different task, explore how an existing agent's "job description" (see point 1) can be expanded to encompass new, related responsibilities.
Consolidation and harmonization: Actively seek opportunities to consolidate overlapping agent functionalities and create a more cohesive, integrated "digital workforce."
“We didn’t want a smarter chatbot, we wanted a colleague. At Byggmax, the goal wasn’t to automate responses, but to embed an AI agent into the team in a way that made sense. That meant giving it a clear role, building in feedback loops, and designing the handoffs between digital and human coworkers that made sense for the team, and Byggmax's customers.”
- Jonas Röst
You’re about to build a digital workforce
Agentic AI projects don’t fail because the models are weak. They fail because the systems around them, processes, org structures, governance, and culture, aren’t designed to support autonomy, accountability, or growth.
Most enterprises have spent years learning how to manage people. Few are ready to manage non-human peers. And yet that’s exactly what agentic AI requires: workflows that support handoffs, oversight structures that mirror team dynamics, and feedback loops that drive continuous learning. You’re not scaling code. You’re scaling a new kind of colleagues.
That means onboarding with intent, assigning responsibilities with clarity, reviewing performance with structure, and designing escalation and improvement paths that don’t rely on hope or hacks.
The shift from co-pilots to agents is organizational. Co-pilots assist and humans remain accountable. Agents own and act independently. If they’re going to do real work, they need the same infrastructure and expectations you’d provide any teammate.
If you’re past the co-pilot phase, the next question isn’t whether the model is good enough. It’s whether your system is. How will you onboard, govern, and evolve your digital colleagues, just like your human ones?
That’s the real maturity curve. Not just smarter models. Smarter organizations [13].
