Agentic AI: Decoding myth vs. fact
Agentic AI is taking over conversations from boardrooms to lunchrooms. For leadership, the areas of concern could be how this technology can be better utilized to improve business processes; for employees, it could mean concerns about whether agentic AI will replace them.
This is why we recently hosted a webinar featuring two people who listen in on these conversations, Richard Mendis, Chief Marketing Officer at Bytemethod.ai & Board Member, Fortude, and Shiraz Omar, Board Member & VP of Digital at Fortude, to cut through the noise and level-set on one of the most exciting yet misunderstood areas: agentic AI. This blog post serves as a summary of that discussion.
In this blog, we will define what agentic AI truly is, dissect the common misconceptions, and see how Fortude has built these capabilities into Charlie – our very own AI companion.
What is Agentic AI?
To truly understand the technology, we need to start by clarifying terminology. We define agentic AI as AI that focuses on completing a goal or task with its own agency.
What do we mean by agency? Agency means the AI can:
- Think for itself to come up with a plan.
- Determine the necessary actions and tools to use.
- Evaluate its results before deciding if the response is sufficient.
These agents typically leverage Large Language Models (LLMs) or generative AI at various steps to perform actions, devise plans, and evaluate outcomes.
Agentic AI can be implemented in a few ways:
- Interactively: As a workflow that runs interactively with a human (similar to going into ChatGPT).
- Enterprise mode: Running behind the scenes, perhaps waking up daily at a set time or being triggered from another enterprise application.
Decoding the 5 myths vs. facts
Now, let’s dive into the essence of the discussion, understanding the myths surrounding agentic AI and the realities as they stand today.
Myth 1: Agentic AI can fully replace humans
The Reality: Human validation and oversight are paramount
While AI excels at certain tasks and can automate them to the point where human intervention isn’t needed for that specific task, the entire human role in a job doesn’t necessarily get replaced. This remains a myth for the foreseeable future.
We recommend starting by automating low-judgment tasks and thereby elevating humans to higher-value work. Human oversight is still very important due to the risk of AI hallucination. When implementing these systems, leadership must remind employees that the goal is not to eliminate jobs, but to empower people to grow and scale and be able to do more with the same capacity.
Myth 2: AI agents are prepared to solve any task well
The Reality: General agents are masters of none; specificity wins.
It’s tempting to assume that an agent will solve everything right out of the box, but unfortunately, this is not the case. An agent deployed without tweaking or customization tends to be a jack of all trades and a master of none.
For specific, enterprise use cases utilizing your organization’s data, you absolutely must do training, benchmarking, and experimentation. Tightening the scope and fine-tuning the agent for that domain context will lead to better outcomes. Highly specialized agents perform significantly better.
Myth 3: Technical AI success means adoption will also succeedng
The Reality: Change management is crucial to help people adapt to new ways of working.
Building a great agentic process in a technical environment is only half the battle. Just because it works technically doesn’t guarantee full adoption by the human team. If the agent changes something humans currently do, you will likely still need a human in the loop for judgment calls or regulatory requirements.
You must involve humans early for change management. This is more than just a technical issue; it involves addressing the human fear of losing jobs. Ensure there is alignment of incentives so that the human workers who need to adopt the process are invested in its success. Aligning KPIs to drive human buy-in is also highly recommended.
Myth 4: If it works in testing, it will work in production
The Reality: AI is non-deterministic and requires rigorous guardrails.
Production environments expose all sorts of issues, unexpected hallucinations and boundary cases that testing didn’t encounter. Unlike classical software, where you expect the same result every time, AI is non-deterministic.
You need to put guardrails in place to ensure that the variability in the output is within an acceptable comfort level. When you stress test, try to consider all possible scenarios, as real-world use cases always present multiple edge cases. Additionally, tightening the scope of each agent’s responsibilities can help manage variability and maintain control over outcomes.
Myth 5: Once AI agents are in production, the work is done
The Reality: Constant monitoring is essential for stability.
If you have reached production, your work isn’t over. The underlying technology is evolving incredibly fast, with new models being released almost weekly. Furthermore, models can experience drift over time, starting to produce different results.
You must constantly monitor and revisit new models to see if they can improve your agent outcomes. This means your agentic flow must be architected in a way that makes it easy to benchmark and swap models in and out.
When monitoring and evaluating, we typically focus on three dimensions:
- Quality: Measured by metrics like accuracy, precision, and recall.
- Cost: Finding models that perform better at a lower cost for certain tasks (often requiring a mixture of different models).
- Speed: Ensuring the agent performs within the required time constraints.
Charlie: Building agentic capabilities into our AI companion
At Fortude, we have taken these realities to heart and built specialized agentic capabilities into Charlie. Here’s a summary of its capabilities:
1. Signal-based inventory levelling
Retailers often struggle with stock-outs in some stores and excess in others. This agent forecasts demand using sales patterns and demand signals, then recommends proactive stock balancing across locations, reducing lost sales and carrying costs.
In short: Predict demand, rebalance inventory, minimize waste, and avoid stock-outs.
2. Model Context Protocol (MCP) for Infor ERP
Every new automation in Infor M3 required custom API work. Our MCP layer standardizes how AI interacts with Infor, enabling secure, context-aware ERP actions through natural language prompts.
In short: A reusable AI-ready interface for Infor M3 that accelerates automation and ensures secure access.
3. Demand forecasting for fashion
Fashion planners often rely on manual, backward-looking forecasting. This agent combines internal sales and inventory with external signals like weather, promotions, and trend data to produce SKU-level forecasts and reorder suggestions.
In short: AI forecasts future demand using real-time signals and automates PO recommendations for planners.
4. M3 release impact analysis
ERP teams spend hours reviewing release notes and mapping updates to custom configurations. Charlie’s multi-agent workflow reads release notes, checks client setups, and creates Jira tickets automatically.
In short: Automates release note review and impact analysis, cutting effort by up to 90% and preventing missed changes.
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If you have been considering the possibilities of agentic AI or feeling nervous about its future, we hope this blog helps you navigate those important questions. We also hope it gives you the clarity to plan your next steps based on the realities outlined here.
Fortude’s teams have the expertise to help you build the right data foundation for AI and tap into the agentic capabilities of Charlie, our AI companion. Learn more about preparing for a future powered by agentic AI by booking a call with our team. And if you missed the webinar, you can watch it here.
FAQs
Data is the foundation of agentic AI. High-quality, well-structured data enables agents to make context-aware decisions, learn from outcomes, and act autonomously with precision. Without reliable data pipelines and governance, agents may hallucinate or make poor decisions. Building a strong data foundation ensures accuracy, trust, and scalability in your AI-driven processes.
Yes, assigning agents to specific, well-defined tasks is often the most effective approach. Specialized agents perform better because their scope is narrow and optimized for a given domain. To enable this, organizations should modularize workflows, integrate AI through APIs or orchestration layers, and ensure systems can securely exchange data across agents and enterprise platforms.
Agentic AI adoption is growing across industries like retail, manufacturing, logistics, and financial services. Retailers use agents for demand forecasting and stock optimization, manufacturers for predictive maintenance, and financial firms for automated risk analysis. Early adopters focus on high-impact, repetitive processes where autonomy and decision-making speed create measurable value.
Common pitfalls include deploying agents without a clear scope, weak data foundations, or insufficient human oversight. Many also overlook change management, failing to prepare employees for new workflows. Another challenge is over-reliance on testing environments without real-world stress tests. Success requires strong guardrails, continuous monitoring, and alignment between technical and human factors.
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