# 7 mistakes IT leaders make when deploying AI agents > Source: > Published: 2026-08-11 10:01:00+00:00 CIOs are under pressure to deploy more AI agents and demonstrate their business value. But a “move fast and break things” approach can lead to [rogue AI agents](https://drive.starcio.com/2025/07/rogue-ai-agents-cios-govern-agentic-ecosystem/), [AI debt](https://www.cio.com/article/4178324/7-sources-of-ai-debt-and-how-to-avoid-them.html), business impacts, and compliance issues. Avoiding mistakes starts with a strong plan and foundational practices. CIOs must have a process to evaluate an [AI agent’s business value](https://drive.starcio.com/2026/02/why-chaotic-ai-experiments-arent-producing-business-value/) before investing in its development. Buy versus build is a consideration; organizations can leverage [AI agents deployed on SaaS platforms](https://drive.starcio.com/2025/10/ai-agents-definitive-guide-saas-security-titans/) or consider developing them [using vibe coding](https://www.cio.com/article/4148288/vibe-coding-your-own-enterprise-apps-is-edgy-business.html) or spec-driven development practices. When building AI agents, IT leaders should [develop the security model](https://drive.starcio.com/2026/06/securing-ai-agents-before-the-poc/) before implementing the POC and ensure [robust observability](https://www.cio.com/article/4176067/the-ai-governance-imperative-you-cant-afford-to-ignore.html) is in place. Top CIOs and CISOs communicate [non-negotiable AI agent release criteria](https://www.infoworld.com/article/4105884/10-essential-release-criteria-for-launching-ai-agents.html), providing teams standards for what meets compliance, security, and operational requirements. Organizations scaling from a few to hundreds of production AI agents must also develop [AgentOps practices](https://www.infoworld.com/article/4100507/5-key-agenticops-practices-to-start-building-now.html) across incident management, [modelops](https://www.infoworld.com/article/3681208/5-risks-of-ai-and-machine-learning-that-modelops-remediates.html), and end-user feedback. Guilherme Soubihe, co-founder and CEO at Latitude.sh, says, “Your first concern shouldn’t be avoiding mistakes when you deploy agents; it should be avoiding them before you deploy at all.” Deployment mistakes can be made even with the best-laid plans. The following seven mistakes occur before building, during the engineering process, and once deployed. Matt Graney, chief product officer at Celigo, says many organizations treat agents as a default solution for processes that already work with known inputs, consistent outputs, and reliable execution at scale. “Agents add cost, latency, and variability that erode exactly what made those processes reliable. Before deploying an agent, ask whether the task requires judgment, or does it just need to work?” Graney says. Even when an existing workflow requires modernization, deterministic forms of automation, predictive models, and integrations may be more effective solutions. Another concern with agentic AI solutions is costs, which can be hard to predict as [frontier model pricing changes](https://pricepertoken.com/trends). “A good AI agent has to be four things at once: cost-efficient, fast, accurate, and secure,” says Vinod Jayaraman, co-founder and CTO at NeuBird AI. “Companies routinely underestimate cost, and I’ve watched teams ship an agent that was fast and accurate, only to pull it weeks later because it was too expensive to run at scale.” **How to avoid the mistake:** Have a defined process to evaluate ideas based on business value and an architect’s review before locking in building or buying AI agents as the solution. One of the biggest gaps in data governance is identifying data owners, and many chief data officers have to backpedal their way to assign responsibilities and educate owners about their roles. AI agents also need owners, especially ones automating all or parts of decision-making in business-critical areas. What happens when AI agents make incorrect or suboptimal decisions? Someone has to own the outcomes, and it’s a best practice to define the governance model well before any AI experiments are commissioned. CIOs should also partner with risk management to establish criteria for when AI augmentation of humans is mandatory, when human-in-the-middle is required, and when AI agents can have autonomy. “Too many enterprises launch agents with no named owner, no exception queue, and no plan for quality drift,” says Anirudh Shah, CTO at MediaMint. “They treat autonomy like a switch, going full-auto after a demo instead of earning trust, decision by decision. Start with tightly scoped micro-tasks, human oversight, measurable trust thresholds, and knowledge that expires unless revalidated.” As organizations deploy [MCP servers](https://www.infoworld.com/article/4124612/5-requirements-for-using-mcp-servers-to-connect-ai-agents.html) and enable agent-to-agent collaboration, CIOs have more complexity in defining decision-making authorities. Kandarp Desai, CTO at Xactly, says, “This accountability gap becomes especially risky in multi-agent systems, where no single agent is ultimately responsible for the final result. Before deploying, you must answer: When the agent makes an error, who is responsible, and is it possible to trace back its decision process?” **How to avoid the mistake:** Clearly establish AI agent owners and review decision-making risks and costs. These factors should be evaluated against guidelines for deciding when to automate and where in workflows to delegate to people. It’s easy to attend conferences and get excited about [how AI agents are shaping the future of work](https://www.cio.com/article/4189693/how-ai-agents-are-shaping-the-future-of-work.html). But CIOs have to perform a reality check with business leaders, because commissioning AI agents on top of poor data quality and dysfunctional business processes can lead to costly programs and deployment disasters. “Point an agent at duplicate records, conflicting definitions, and documents nobody has updated in two years, and it won’t clean any of that up; it will confidently act on all of it, then repeat the same mistake at scale,” says CJ Combs, AI strategy executive at Columbus Global. “The agent didn’t fail; it surfaced the data and governance debt you already had, now compounding at machine speed across every workflow it touches.” CIOs are investing in [data fabrics](https://www.infoworld.com/article/4182695/develop-smarter-ai-agents-with-data-fabrics.html) and addressing [data management debt](https://drive.starcio.com/2026/06/data-management-debt-ai-era-cios/) as prerequisites for deploying AI agents. “One of the biggest challenges organizations face with agentic AI is scaling too soon before establishing a unified business data foundation,” says Michael Ameling, president of SAP Business Technology Platform and member of the extended board at SAP. “Agents depend on trusted business data, business context, and governance to operate reliably across the enterprise.” **How to avoid the mistake:** [Measure data quality](https://www.infoworld.com/article/2336075/3-data-quality-metrics-dataops-should-prioritize.html) and establish a minimal trust score for data sets used for training AI models or for providing context to AI agents during runtime. The organization’s subject matter experts often have access to a wide range of platforms and data sources. Experts advise against providing AI agents with the same or greater level of access to information. “AI agents actually behave more like semi-trusted external contractors or unvetted employees,” says Shad Malloy, senior managing consultant at Bishop Fox. “You wouldn’t hand a new intern unrestricted access to your email, file share, and accounting platform, so there’s no reason to grant an agent broad permissions either.” Organizations need policies and platforms to secure confidential data, ensure compliance with data privacy regulations, and protect intellectual property. “If an agent can reach sensitive data, production systems, or high-impact workflows by default, governance becomes reactive instead of architectural,” says Gal Ordo, co-founder and CPO at Native. “Define the zones the agent can operate in, the boundaries it can cross, and the baselines that must always hold, so teams can move quickly without creating risk that scales faster than they can control.” **How to avoid the mistake:** Businesses in regulated industries and others deploying AI agents with sensitive data will need AI governance platforms to map data sources to AI agents and centralize data access rules. Robust regression tests deployed in [continuous testing](https://www.infoworld.com/article/3705049/3-ways-to-upgrade-continuous-testing-for-generative-ai.html) and automated [continuous deployment](https://www.infoworld.com/article/3663055/are-you-ready-to-automate-continuous-deployment-in-cicd.html) are the goal for applications and APIs. Extend these objectives when building, testing, and deploying AI agents to account for variability in data, models, and real-time inference context. “The most common mistake is treating an agent like a traditional app: You test it before deployment, sign off, and assume it’s safe in production,” says Sanmi Koyejo, co-founder and head of AI at Virtue AI. “But agents are non-deterministic and stateful, so the same request can trigger a different sequence of tool calls every time. Pre-deployment testing can’t enumerate those paths, and worse, a chain of individually permitted actions can still add up to data exfiltration or an unauthorized transaction.” Koyejo suggests that testing also needs runtime enforcement, checking every tool call before it executes and either blocking or alerting on risky actions as they occur. Patrick Phillips, CIO at Vasion, recommends CIOs build four controls before deploying AI agents. **How to avoid the mistake:** Blur the lines between testing and monitoring AI agents, as their recommendations and actions should be evaluated consistently across both environments. The CHRO may own the AI agents for recruitment and define their decision-making authorities, but what about the recruiters and hiring managers? [AI change management](https://www.cio.com/article/4082282/preparing-your-workforce-for-ai-agents-a-change-management-guide.html) programs must consider the business objectives related to decision-making authorities, evaluate AI agent accuracy, and gain buy-in from the people most directly impacted by workflow changes. “The biggest mistake I see enterprises make is deploying AI agents without defining a clear ‘human in the loop’ escalation model before go-live,” says Krish Mantripragada, chief product and technology officer at Seismic. “Teams spend a lot of time mulling over what the agent can do autonomously but skip the harder question: at what confidence threshold, business risk level, or action type does it stop and ask?” **How to avoid the mistake:** Deploying AI agents is only the start of its lifecycle of evolving business processes. Leaders must consider how to help employees adapt to workflow changes, and then develop a skill set for [managing AI agents](https://drive.starcio.com/podcast/managing-ai-agents-new-skills-operating-models-and-tools/). Many of the mistakes add up to one critical AI agent reality: Deployments are not the endgame; business value is the goal; and what agents respond to in production may look very different from what they were exposed to during testing. Iris Adae, VP of data and analytics at KNIME, says, “The mistake I see most when deploying AI agents is assuming the pilot is the finish line. Agents behave very differently in a curated pilot than in production, where edge cases and messy integrations finally surface.” CIOs are plagued with [technical debt](https://www.cio.com/article/3850777/7-types-of-tech-debt-that-could-cripple-your-business.html), and one source is when businesses stopped funding a technology’s maintenance and support. That same approach can not only lead to [AI cost debt](https://drive.starcio.com/2026/06/ai-cost-debt-how-finops-helps-cios/) but also erode efficiencies and increase operational risks. **How to avoid the mistake:** While there’s been significant debate on business-unit chargeback models for production applications and SaaS, the approach must be considered for AI agents deployed to production. CIOs looking to deploy more AI agents to production need a well-defined operating model that addresses a continuous lifecycle of delivery, deployment, measurement, feedback, and improvement.