It might be inevitable to meet a person working in AI without mentioning AI agents, given how popular the technology is right now. However, they still remain outside the mainstream.
AI agents appear more powerful and useful than chatbots, yet their real-world impact remains limited despite their prevalence. Even organisations seeing productivity improvements are not going all in. The hesitation isn’t mainly about the worth of AI agents, but rather about the resources needed to develop and run them. Enterprises regard this as a complex engineering challenge, not a straightforward plug-and-play upgrade.
Ashish Kumar, the chief data scientist at Indium Software, in an exclusive interview with AIM, says that the tech works, but the skill gap is real. Agentic AI needs more than prompts and APIs. It requires thoughtful design, orchestration, modularity, and people who understand both software and business logic.
The Role of Forward Deployed Engineers and Modular Agents
Agentic systems differ from traditional bots by not adhering to fixed rules. Instead, they make decisions driven by goals rather than predefined instructions. Kumar highlights that this difference diminishes the usefulness of traditional metrics like accuracy. Ultimately, what matters is whether the system reliably and transparently accomplishes its task.
Kumar states these systems usually succeed 90-95%, but the remaining 5% are challenging edge cases that delay reaching 99%. Since these are crucial for business, even a 95% success rate isn’t enough.
So, taking from 95 to 99 is the most difficult part, and that is why, especially in agentic AI solution development, a new term is emerging—it’s called Forward Deployed Engineers,” Kumar said.
Kumar explained that their role is to anticipate edge cases and steer system behaviour from the start. He emphasised that agents must be deliberately designed to be modular, with each one focused on a single task, to ensure effective implementation.
Cost, Complexity, and What It Takes to Build
Kumar mentioned that the cost factor should not be considered a major concern regarding the slow adoption of AI agents.
He explained that in internal use cases, API costs are manageable. However, for consumer-facing agents that depend on high-volume LLM calls, the costs may escalate rapidly. As foundation models develop, these costs should decrease. For now, they remain a limiting factor.
Kumar admitted that integration is another challenge. Building agentic systems means stitching together LLMs, vector databases, orchestration layers, memory modules, and enterprise APIs.
The architecture of an agentic system resembles microservices, being highly flexible but also challenging to maintain. “It’s more effective than a traditional automation,” Kumar said, “but it’s also much difficult to build.” “The downside of that is that to architect a very good agentic AI solution, you need really skilled people.”
Designing context-aware agents with memory, validation, and multi-agent handoffs takes weeks to master. Unlike RPA tools with drag-and-drop interfaces, agentic AI demands deep fluency across frameworks like LlamaIndex, CrewAI, LangChain and protocols like MCP.
He noted agent adoption may improve as complexity decreases. For early investors, the benefits are clear. For others, it’s about time, training, and confidence. Despite all this, he believes AI agents are being adopted more than chatbots and RAG systems.
Tech Used to Build an Agent and Its Future
At Indium Software, Kumar highlights that they use CrewAI for orchestration, Claude for code-heavy tasks, and Gemini for multimodal inputs.
LlamaIndex supports document parsing, while FastAPI and MCP protocols handle enterprise system integration.
Development often starts locally with Ollama or Hugging Face models. These initial prototypes later connect to OpenAI or Claude via API, depending on the task. For managing state and memory, Redis, vector databases, and knowledge graphs work together.
“APIs must be inherently MCP compliant. When developing APIs, they should automatically be converted to MCP-compliant versions,” Kumar stated.
The post AI Agents Work, But Why Aren’t They Mainstream Yet? appeared first on Analytics India Magazine.
