AI, LLMs, and applied ML.
Senior-engineer field notes on AI, LLMs, agents, and applied machine learning by Ercan Ermis.
All posts
Claude Code in CI: Letting an Agent Fix the Build
Running a coding agent in your pipeline is easy. The engineering is in the guardrails: what it can touch, when it stops, and who reviews what it proposes.
Agents on Call, Part 2. The Foundation: Terraform Before Tokens
Before a single Bedrock token flows: the ops-tooling account, the two spoke IAM roles, model access, and the inference-profile decision, all in Terraform.
The Context Window Is Not Your Friend
A huge context window is not a replacement for retrieval. Recall degrades as the prompt grows, cost scales with every token, and the middle gets skimmed.
AWS Monthly (May '26): Agents Get a Wallet
May 2026 on AWS: AgentCore Payments lets agents transact, and the Agent Toolkit for AWS plus a GA managed MCP server harden the toolchain that builds them.
Agents on Call, Part 1. The Scenario: Why an Ops Team Hires Agents
A mid-size SaaS drowns in on-call toil and decides to hire agents. Part 1: the scenario, the stack decision, and the AWS Bedrock architecture we will build.
Logging Prompts Without Logging PII
You need prompt logs to debug an LLM app but cannot keep raw PII in them. Redact before storage with Comprehend, then set retention so logs age out on schedule.
When Haiku Beats Opus: Model Right-Sizing on Bedrock
Defaulting every call to Opus is how LLM bills balloon. Route by task class: Haiku for the mechanical majority, Opus as the escalation path for hard cases.
Agentic RAG Is Mostly Latency You Don't Need
Agentic RAG loops through retrieval hops, each a model round trip. For most questions one good query wins. Reach for the loop only when it earns the latency.
Evals Before Agents: You Can't Ship What You Can't Score
Without an eval harness, every agent change is a vibe check. Build the scoreboard before the agent, and treat the LLM-as-judge as a component that can be wrong.
Semantic Caching: Two Different Questions, One Answer
Semantic caching returns one stored answer for two differently worded questions. It cuts cost and latency, but a false hit serves a confidently wrong reply.
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Field notes from production systems. EKS, IAM, Terraform at organization scale, observability, cost optimization.
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