生产级AI代理构建：数据管道、控制与可观测性是关键

Production-grade agents live or die on data plumbing, controls, and observability—not on model choice. The doc-to-chat pipeline below maps the concrete layers and why they matter.

What is a “doc-to-chat” pipeline?

A doc-to-chat pipeline ingests enterprise documents, standardizes them, enforces governance, indexes embeddings alongside relational features, and serves retrieval + generation behind authenticated APIs with human-in-the-loop (HITL) checkpoints. It’s the reference architecture for agentic Q&A, copilots, and workflow automation where answers must respect permissions and be audit-ready. Production implementations are variations of RAG (retrieval-augmented generation) hardened with LLM guardrails, governance, and OpenTelemetry-backed tracing.

How do you integrate cleanly with the existing stack?

Use standard service boundaries (REST/JSON, gRPC) over a storage layer your org already trusts. For tables, Iceberg gives ACID, schema evolution, partition evolution, and snapshots—critical for reproducible retrieval and backfills. For vectors, use a system that coexists with SQL filters: pgvector collocates embeddings with business keys and ACL tags in PostgreSQL; dedicated engines like Milvus handle high-QPS ANN with disaggregated storage/compute. In practice, many teams run both: SQL+pgvector for transactional joins and Milvus for heavy retrieval.

Key properties

Iceberg tables:

pgvector:

Milvus:

How do agents, humans, and workflows coordinate on one “knowledge fabric”?

Production agents require explicit coordination points where humans approve, correct, or escalate. AWS A2I provides managed HITL loops (private workforces, flow definitions) and is a concrete blueprint for gating low-confidence outputs. Frameworks like LangGraph model these human checkpoints inside agent graphs so approvals are first-class steps in the DAG, not ad hoc callbacks. Use them to gate actions like publishing summaries, filing tickets, or committing code.

Pattern: LLM → confidence/guardrail checks → HITL gate → side-effects. Persist every artifact (prompt, retrieval set, decision) for auditability and future re-runs.

How is reliability enforced before anything reaches the model?

Treat reliability as layered defenses:

Language + content guardrails:

PII detection/redaction:

Access control and lineage:

Retrieval quality gates:

How do you scale indexing and retrieval under real traffic?

Two axes matter: ingest throughput and query concurrency.

Ingest:

Vector serving:

SQL + vector:

WHERE tenant_id = ? AND acl_tag @> ... ORDER BY embedding <-> :q LIMIT k

Chunking/embedding strategy:

For structured+unstructured fusion, prefer hybrid retrieval (BM25 + ANN + reranker) and store structured features next to vectors to support filters and re-ranking features at query time.

How do you monitor beyond logs?

You need traces, metrics, and evaluations stitched together:

Distributed tracing:

LLM observability platforms:

Continuous evaluation:

Add schema profiling/mapping on ingestion to keep observability attached to data shape changes (e.g., new templates, table evolution) and to explain retrieval regressions when upstream sources shift.

Example: doc-to-chat reference flow (signals and gates)

Ingest:

Govern:

Index:

Serve:

HITL:

Observe:

Why “5% AI, 100% software engineering” is accurate in practice?

Most outages and trust failures in agent systems are not model regressions; they’re data quality, permissioning, retrieval decay, or missing telemetry. The controls above—ACID tables, ACL catalogs, PII guardrails, hybrid retrieval, OTEL traces, and human gates—determine whether the same base model is safe, fast, and credibly correct for your users. Invest in these first; swap models later if needed.