I wanted to share some hard-learned lessons about deploying multi-component AI agents to production. If you've ever had an agent fail mysteriously in production while working perfectly in dev, this might help.

The Core Problem

Most agent failures are silent. Most failures occur in components that showed zero issues during testing. Why? Because we treat agents as black boxes - query goes in, response comes out, and we have no idea what happened in between.

The Solution: Component-Level Instrumentation

I built a fully observable agent using LangGraph + LangSmith that tracks:

Component execution flow (router → retriever → reasoner → generator)

Component-specific latency (which component is the bottleneck?)

Intermediate states (what was retrieved, what reasoning strategy was chosen)

Failure attribution (which specific component caused the bad output?)

Key Architecture Insights

The agent has 4 specialized components:

Router: Classifies intent and determines workflow

Retriever: Fetches relevant context from knowledge base

Reasoner: Plans response strategy

Generator: Produces final output

Each component can fail independently, and each requires different fixes. A wrong answer could be routing errors, retrieval failures, or generation hallucinations - aggregate metrics won't tell you which.

To fix this, I implemented automated failure classification into 6 primary categories:

Routing failures (wrong workflow)

Retrieval failures (missed relevant docs)

Reasoning failures (wrong strategy)

Generation failures (poor output despite good inputs)

Latency failures (exceeds SLA)

Degradation failures (quality decreases over time)

The system automatically attributes failures to specific components based on observability data.

Component Fine-tuning Matters

Here's what made a difference: fine-tune individual components, not the whole system.

When my baseline showed the generator had a 40% failure rate, I:

Collected examples where it failed

Created training data showing correct outputs

Fine-tuned ONLY the generator

Swapped it into the agent graph

Results: Faster iteration (minutes vs hours), better debuggability (know exactly what changed), more maintainable (evolve components independently).

For anyone interested in the tech stack, here is some info:

LangGraph: Agent orchestration with explicit state transitions

LangSmith: Distributed tracing and observability

UBIAI: Component-level fine-tuning (prompt optimization → weight training)

ChromaDB: Vector store for retrieval

Key Takeaway

You can't improve what you can't measure, and you can't measure what you don't instrument.

The full implementation shows how to build this for customer support agents, but the principles apply to any multi-component architecture.

Happy to answer questions about the implementation. The blog with code is in the comment.