Practice · 2023–now · AI Present
Agent ops / LLM observability
Tracing, cost caps, and prompt versioning for production LLM features — mostly constrained tool loops, not autonomous agents. Datadog for tokens.
Why Agent ops / LLM observability stuck
Agent ops stuck because token bills and silent regressions appear the moment a demo hits real users. The autonomous-agent buzzword faded; the need for traces, eval hooks, and spend alerts did not. It failed as theater when teams bought tools without defining success metrics first.
Compare with
Related
Practice · 2023–now
LLM eval pipelines
Regression tests for nondeterministic models that actually fail the build. The unglamorous CI that separates demos from products.
Framework · 2023–now
Multi-agent orchestration hype
LangGraph, CrewAI, AutoGPT cosplay — then vendor harnesses shipped the same loop as an API. Open-source swarms faded; leased orchestration stuck.
$ Autonomous agent swarms burned API budgets on coordination loops that a single prompt and a cron job would have handled. Debugging "which agent lied" became the new on-call sport.
Practice · 2021–now
AI pair programming
Coding agents that edit the repo — now table stakes. Generation outran review capacity; ownership is the bottleneck.
Architecture · 2024–now
Model routing / tiered inference
Cheap model drafts; frontier model escalates. The boring cost-control layer that survived the agent hype.
Platform · 2025–now
Vendor agent APIs
The Codex/Claude loop as a product SKU — durable sessions, compaction, subagents, and a sandbox you do not own.
$ Teams replaced a weekend of LangGraph glue with a managed session API, then discovered residency, recovery, and kill switches all live on the vendor side of the harness.