The 10 biggest agentic AI challenges and how to fix them
The very traits that make agentic AI such a promising enterprise solution—autonomy and adaptability in real-time environments—are the same traits that make it so challenging to implement successfully.
Agentic AI—known as AI agents—can reason, manage its own processes and tool usage, and operate across platforms to execute tasks end-to-end without human intervention. This autonomous behavior makes AI agents an attractive option for a growing set of frontline enterprise tasks, but it also introduces new challenges that organizations must overcome to scale AI successfully.
A recent RAND Corporation study found that over 80% of AI projects fail to reach production—a rate nearly double that of typical IT projects. The reported causes range from poor data quality to weak infrastructure to fragmented workflows. Despite this, a growing number of enterprises are proving that agentic AI can succeed when implemented with discipline.
This article explores the most common failure modes of agentic AI, and provides practical strategies from business leaders for effective AI design, oversight, and governance. This way, your teams can build AI agents that are not only powerful—but resilient, reliable, and enterprise-ready.
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Understanding agentic AI and its challenges
AI agents are autonomous systems that can act on behalf of users. Unlike earlier AI, they can reason, coordinate, plan action, and execute tasks across digital environments in pursuit of goals set for them—all without human intervention.
But the more agency that agents have in complex workflows, the more chances for failure. Unlike model-based applications like ChatGPT, AI agents are modular systems composed of multiple components—reasoning engines, orchestration layers, APIs, and knowledge stores. Each of these modules introduces new potential points of fragility and risk.
For example, the final output of an AI agent for customer service might be a text response to a customer query. However, the underlying agentic workflow may involve several intermediate steps: multi-step reasoning, calling an API, querying a customer database with retrieval-augmented generation (RAG), and formatting the response. If any one of those steps falters, both operations and the customer experience suffer.
As Forrester noted in its 2025 Model Overview Report:
“As enterprises adopt AI agents and agentic systems, they discover that these systems fail in unexpected and costly ways. These failures do not follow the patterns of traditional software bugs; they emerge from ambiguity, miscoordination, and unpredictable system dynamics.”
In short, the difference between failed and successful agentic AI projects doesn’t stem from weak models—but from undefined agentic workflows, poor plumbing, and unprepared people. To unlock the full potential of AI agents without incurring risk and lost trust, enterprises must first understand the unique challenges that come with building and scaling agentic systems.
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10 agentic AI challenges and how to fix them
Reviewing recent research from Forrester, McKinsey, RAND, and other sources reveals clear patterns of why agentic AI projects fail—but also succeed. Here are the most common AI agent challenges, and clear strategies to fix them:
1. Misunderstanding the problem
Challenge: It's still early days for AI, and stakeholders often misidentify the business problem AI agents should solve. Per RAND, this is the top reason AI projects fail: leaders are misaligned or unclear about the domain context and project goals.
Solution: Align leadership and technical teams on project purpose and domain context from the start, and define KPIs rooted in real-world business problems, not abstract technical goals (like F1 model performance).
2. Data issues
Challenge: Lack of clean, high-quality, and accessible data is a major driver of AI agent failure. According to Informatica’s 2025 CDO Insights Report, 43% of AI leaders cite data quality and readiness as their top obstacle. For example, outdated training data can lead to inaccurate answers in customer support interactions, while poor data pipelines can cause agents to hallucinate—leading to unreliable outputs that erode customer trust.
Solution: Invest in data readiness and data governance early, including extraction, normalization, metadata, quality dashboards, and retention controls. This helps ensure agents have the clean, integrated, and contextual data they need to operate reliably.
3. Focusing on tech over business problems
Challenge: Organizations too often fixate on choosing the right AI framework or model rather than ensuring agentic AI addresses their persistent business pain points. Teams may chase higher model accuracy scores, for instance, while neglecting workflow design and integration. As a result, by the time projects reach business review, compliance hurdles feel insurmountable, and ROI remains unproven. In fact, 40% of agentic AI projects are projected to be scrapped by 2027 for failing to link back to measurable business value, according to Gartner.
Solution: Anchor agentic AI initiatives to clear operational and customer pain points from the start. Define KPIs based on real-world outcomes (reduced resolution times, boosting customer satisfaction). By focusing on solutions that lower costs and remove friction, enterprises can prove value early and avoid chasing technical capabilities for their own sake.
4. Fragmented execution
Challenge: Siloed teams can create organizational friction that hampers execution. For instance, product teams chase features, IT teams shore up security, and legal drafts AI compliance policies—often without shared success metrics or coordinated timelines. The result of these disconnected efforts and shadow IT (duplicate systems, orphaned models, redundant data stores) is wasted resources, reduced data quality, and hampered governance.
Solution: Centralize AI oversight and governance to maximize alignment, innovation, and performance. Formalize roles, consolidate platforms, and adopt AI frameworks that enforce visibility, compliance, and shared standards, possibly as part of an agentic AI governance framework.
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5. Inadequate infrastructure
Challenge: Organizations may lack the scalable platforms, clear APIs, and orchestration layers needed to support enterprise-grade AI agents. Without robust data plumbing and integration-ready infrastructure, AI agents can’t pursue complex business goals across systems to completion, and so falter due to “immature autonomy.”
Solution: Invest heavily in agent-ready infrastructure before scaling AI pilots. Pairing these investments with agentic governance frameworks (like Sendbird Trust OS) provides both the connective tissue to make AI agent systems not only robust and scalable, but safe and responsible.
6. Workflow & integration failures
Challenge: Poor integration with legacy systems and rigid workflows can cause agents to break down mid-task, especially for cross-system workflows. For example, Salesforce admitted its Einstein Copilot struggled in pilots because it couldn’t reliably navigate across customer data silos and legacy CRM workflows, forcing costly human intervention.
Solution: Rather than “bolting on” AI to legacy processes, re-architect workflows around AI agents before plugging them in. McKinsey's 2025 State of AI Survey found that organizations reporting "significant" ROI from AI projects are twice as likely to have redesigned end-to-end workflows before deploying AI.
7. Balancing human + AI collaboration
Challenge: Full-on AI automation is an alluring idea, but in practice, augmenting humans with AI agents tends to deliver better outcomes, especially in customer experience use cases. By over-automating, organizations risk alienating customers who still expect a human touch. Klarna, for instance, initially touted that its AI agent handled 80% of customer interactions. But after customers complained about the lack of human fallback, the company reverted to amplifying its human capabilities with AI, not replacing them.
Solution: AI leaders tend to design choreographed workflows where AI agents handle FAQs, routine tasks, and upsells, while humans remain in the loop for exceptions or emotionally charged interactions. This involves defining which actions stay human, building in override paths, and capturing user feedback regularly.
8. Task complexity exceeds capability
Challenge: While leaders should choose enduring problems for agentic AI to solve, this evolving technology can be applied to problems too complex for its current capabilities, setting projects up for failure. Importantly, many “agentic” AI companies are overhyped (known as “agent washing”) and can’t reliably deliver enterprise-grade outcomes.
Solution: Business leaders should understand AI's limitations and convene technical experts as needed to assess project feasibility. Also, start with well-defined tasks that AI can realistically automate, then scale to more complex applications once reliability is proven.
9. Overlooking people and processes
Challenge: Many organizations treat AI agent deployment as a purely technical rollout, overlooking the organizational changes required for success. Both RAND and Gartner identify this as a leading agentic AI challenge: leaders underestimate the need for process change and human alignment, leaving human teams disengaged or resistant.
Solution: Approach AI adoption as a process transformation, not just a technology upgrade. This means upskilling employees, redesigning workflows, and defining how responsibilities are shared between humans and machines. Embedding change management, user feedback loops, and governance structures from the start ensures employees see AI as an enabler—not a threat—accelerating adoption and improving business outcomes.
10. Pilot paralysis
Challenge: Many agentic AI initiatives stall in proof-of-concept mode. The technology performs well in a sandbox, but integration tasks like authentication, compliance workflows, and user adoption are pushed aside until executives ask for a production timeline. By then, the pilot feels too fragile to scale, eroding trust and momentum.
Solution: Treat AI pilots not as experiments, but as products from day one. Successful enterprises assign product managers to AI services, define clear SLAs and SLOs (e.g., “ticket summary accuracy >85% with <5s latency, 95% of the time”), and budget for continuous improvement. With standardized AI observability—event logs, drift detection, and user feedback loops tied into dashboards—agents become living systems with uptime, reliability, and customer satisfaction as metrics. This turns pilots into production-ready assets that evolve with the business rather than dying in isolation.
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How do organizations succeed despite agentic AI challenges?
The agentic AI challenges listed above share a common thread: the AI model rarely breaks, but the underdeveloped infrastructure, strategy, or data management processes around it buckle in real-world scenarios.
The good news is that many organizations like Klarna, Lotte Homeshopping, and others are delivering measurable business value with AI agents. When agentic AI is implemented with discipline, it can reduce costs, improve CX, and unlock new growth.
Enterprises that succeed with AI agents share a few common patterns:
Adopt a phased approach: Scale incrementally. Early pilots should deliver tangible wins, build trust in AI, and fund the next phase of investment. This stepwise approach reduces risk while proving impact.
Treat agents as a virtual workforce: Manage AI agents like organizational assets—via defined roles, performance reviews, and accountability. This includes ongoing agent monitoring, version control, role-based permissions, and lifecycle management, making AI oversight and governance part of routine maintenance instead of a crisis response.
Embed oversight and governance: Humans-in-the-loop isn’t a fallback of AI agents, at present—it’s a feature. For example, one sales team using Microsoft Copilot saw 9.4% higher revenue per seller and 20% more deals closed when humans reviewed AI outputs before execution. Structured AI governance ensures agents act responsibly, consistently, and in line with enterprise standards.
Ultimately, the difference between stalled pilots and successful AI solutions lies not in the power of the technology but in the patterns of execution that support daily AI operations.
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How Sendbird helps you overcome agentic AI challenges
The story of agentic AI is one of promise and pitfalls. It’s not hard to imagine a world where you invest in building an AI voice agent to improve the customer experience, only to see it abandoned after customers lose patience with inaccurate answers resulting from unreliable data pipelines or a lack of oversight.
Sendbird helps enterprises avoid these challenges and build robust, scalable AI solutions. Our AI agent platform has everything teams need to build, deploy, and scale enterprise-ready AI agents—including the trusted infrastructure, powerful tools, and the Trust OS agent governance framework to ensure AI trust and safety.
Combined with our white-glove consultative approach, these capabilities turn AI agents from unproven pilots into reliable assets that operate safely and deliver measurable business value. To learn more, contact sales or request a demo.
