From the threads: AI agents for workflows — What’s working and what’s not

At Fortude, we’re not just following the AI agent conversation, we’re actively shaping it with Charlie, our enterprise-grade AI assistant. Built on a Retrieval-Augmented Generation (RAG) framework, Charlie is evolving from a knowledge assistant into an agentic AI platform with real-world impact.

Unlike general-purpose agents that try to do everything, Charlie is purpose-built for enterprise workflows. Each use case is designed around domain-specific pain points, with built-in safeguards, monitoring, and explainability baked into the design.

Let’s explore how Charlie is solving the pain points that Reddit builders are wrestling with.

1. Inventory levelling agent

Charlie’s inventory management agent uses real-time demand signals to assess stock levels and suggest redistribution between locations. This reduces stockouts and overstock scenarios without human intervention.

• Why it matters: It’s a tightly scoped, high-value task, exactly what Reddit users suggest agents should start with.

2. M3 release impact analysis

When a new Infor M3 release is published, a set of coordinated agents, including document parsers and source code analyzers, automatically identify impacted modules and generate Jira tickets for consultants.

• Why it matters: It removes 80–90% of manual analysis effort, while maintaining traceability and confidence through built-in validation layers.

3. Signal-based demand forecasting

This Charlie agent blends internal ERP data with external signals (e.g., weather, trends) to forecast SKU-level demand for fashion retailers. It recommends POs and shipment timelines in a single automated pass.

• Why it matters: Combines structured planning logic with adaptive context awareness, a step toward trustworthy decision-making.

4. MCP for ERP interoperability

Charlie integrates with Fortude’s Model Context Protocol (MCP), enabling secure and reusable access to Infor ERP systems. It acts as a translator layer, giving AI agents safe access to business data.

• Why it matters: This solves a massive pain point raised in Reddit threads: messy, inconsistent ERP integrations.

5. CharlieX: Democratizing enterprise intelligence

CharlieX, a companion to Charlie, focuses on enterprise analytics. It connects directly to ERP/CRM data and lets business users ask natural-language questions, surfacing patterns, anomalies, and recommendations in seconds.

• Why it matters: This bridges the gap between AI and decision-makers, allowing agents to proactively suggest actions instead of just returning results.

From hundreds of comments and real-world use cases, a few best practices emerge:

• Start narrow: Choose one pain point with clear ROI. Avoid broad, loosely scoped workflows.
• Build feedback loops: Use human-in-the-loop validation, even in production.
• Prioritize observability: Include logging, tracing, and evaluation from day one.
• Structure data access: Use protocols like MCP to define safe, scalable interfaces.
• Accept imperfection: Even a 70% reliable agent can drive ROI if paired with intelligent fallback mechanisms.

The community sentiment is clear: AI agents are promising, but expectations need calibration. They’re not magic wands or full-time employees. But they can become trusted collaborators for narrow, repeatable workflows, especially when paired with structured validation, observability, and human oversight.

At Fortude, we believe the key is intentional design. Charlie isn’t trying to replace your workforce, it’s built to assist it with reliable, proactive, context-aware support.

As AI tooling matures and orchestration stacks become more robust, we expect agent adoption to move from niche experiments to mainstream deployments, with enterprises like ours leading the way.

Reddit threads reveal a grounded reality: AI agents for workflows are not yet plug-and-play, but the right design makes all the difference. Fortude’s Charlie and CharlieX are built with this in mind, blending retrieval intelligence, integration layers, and practical automation into agentic AI that gets work done.