When should enterprises use AI agents vs workflows?

Service·AI Agents

AI Agents

What is an enterprise AI agent?

An enterprise AI agent plans and uses tools toward a goal under permissions, observability, and human approval for consequential actions. It is not unbounded autonomy.

Engineering Evidence

Case Studies

Real architectural decisions and production trade-offs. We don't invent case studies — these are actual engineering records.

Architecture Protocol•AI Architecture

Human-in-the-Loop Architecture for Enterprise AI Workflows

Why "human in the loop" in most enterprise AI systems is a log file nobody reads — and how to engineer real approval gates that run before irreversible actions execute.

Vishal9 min

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Architecture Protocol•LLM Integration

MCP in Production: Architecting Narrow Data Channels for LLMs

How Vasiva runs Model Context Protocol servers in production with least-privilege database roles, tool allow-lists, and audit logging — because the real security boundary is the database credential, not the tool description.

Panneer8 min

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Frameworks

Original Vasiva decision assets

Vasiva Framework

Enterprise AI Readiness Framework

Decide whether an AI initiative is ready for production — before you scale the wrong thing.

Vasiva Framework

AI Agent Production Readiness

When should an enterprise actually deploy an AI agent — and when should it use a workflow instead?

FAQ

About AI Agents

Use deterministic workflows when the multi-step path is known and stability matters. Use agents when open-ended planning is required — then constrain tools, add human gates, and evaluate behavior before production traffic.

Next Steps

Agents with boundaries — or workflows instead?

Select engagements. You work with the engineers who ship Vasiva products.