AI Agents: A Reality Check

AI agents are everywhere in tech discourse. Every startup is building them, every conference features them. But after building several production agent systems, I've learned that the reality is more nuanced than the hype.

What Actually Works

Agents excel at:

**Structured workflows**: When the task has clear steps and decision points

**Information aggregation**: Pulling data from multiple sources

**Repetitive tasks**: Where consistency matters more than creativity

**Augmentation**: Helping humans work faster, not replacing them

What Doesn't (Yet)

Agents struggle with:

**Open-ended problems**: Where the solution space is undefined

**High-stakes decisions**: Where errors are costly

**Complex reasoning**: Multi-step logic with dependencies

**Context switching**: Moving between very different domains

The Architecture That Works

After multiple iterations, here's what I've found effective:

**Clear boundaries**: Define exactly what the agent can and cannot do

**Human in the loop**: For critical decisions, always confirm

**Fallback mechanisms**: When the agent is uncertain, escalate

**Observability**: Log everything, make the agent's reasoning transparent

Real-World Example

We built an agent for customer support triage:

**Success**: 70% of tickets correctly categorized and routed

**Failure**: Couldn't handle edge cases or emotional customers

**Solution**: Agent handles routine, humans handle exceptions

This hybrid approach works better than full automation.

The Cost Reality

Running agents at scale is expensive:

Token costs add up quickly

Latency can be an issue

Error handling requires infrastructure

Monitoring and debugging are complex

Calculate ROI carefully. Sometimes a simple rule-based system is better.

Building Reliable Agents

Key principles:

**Start narrow**: Solve one problem well before expanding

**Test extensively**: Edge cases will break your agent

**Version control prompts**: Treat them like code

**Measure everything**: Success rate, latency, cost, user satisfaction

The Future

Agents will improve, but they won't replace human judgment anytime soon. The winning approach is augmentation: agents handle the routine, humans handle the exceptional.

Build for the reality of today, not the promise of tomorrow.

AI Agents: A Reality Check

AI Agents: A Reality Check

What Actually Works

What Doesn't (Yet)

The Architecture That Works

Real-World Example

The Cost Reality

Building Reliable Agents

The Future

Yash Sarang

Related Articles

Context is a Feature: Building Better AI Applications