GenAI & Agentic AI Development articles.
Enterprise GenAI and agentic solutions on leading LLMs — with AI evaluation, guardrails and human approval designed in.
Latest in GenAI & Agentic AI Development.
AI Agents for Governance, Risk and Compliance: The Emerging GRC Use Case
Governance, risk, and compliance work is repetitive, evidence-heavy, and spread across systems in a way that makes it a natural fit for agentic AI — and also exactly the kind of high-consequence domain where getting the human oversight boundary wrong is expensive.
Read article →Open Agent Protocols: What MCP and Agent2Agent Mean for Enterprise AI Architecture
Every AI agent needs two kinds of connections: to the tools and data it acts on, and to other agents it needs to coordinate with. Open protocols for both are emerging quickly enough that architecture decisions made without them in mind are already starting to look short-sighted.
Read article →AI Agent Observability: Instrumenting Agentic Systems for Debugging and Trust
A traditional application that fails leaves a stack trace. An agent that fails can leave nothing more than a wrong or unexpected final answer, with no visibility into the chain of reasoning, tool calls, and decisions that produced it — which makes observability a non-negotiable, not a nice-to-have, for any production agentic system.
Read article →Multi-Agent Orchestration: Design Patterns for Reliable Agentic Workflows
A single well-built agent is hard enough to make reliable. Coordinating several of them — each with its own tools, context and failure modes — introduces an entirely new category of problems that single-agent design patterns don’t prepare you for.
Read article →Agentic AI in the Enterprise: Where Human Approval Still Belongs
The pitch for agentic AI is autonomy — an agent that plans, acts, and adapts without a human in the loop at every step. The enterprise reality is more nuanced: the question isn’t whether to keep humans involved, it’s exactly where.
Read article →RAG vs Fine-Tuning vs Prompt Engineering: Choosing the Right Approach for Enterprise GenAI
“Should we fine-tune a model for this?” is usually the wrong first question. The right first question is what kind of knowledge gap you’re actually trying to close — and that determines whether prompt engineering, RAG, fine-tuning, or some combination is the right tool.
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