Agent-oriented software engineering
lastmod 2026-04-17created 2026-03-022 edits
Categories
What is agentic coding?
- Agentic Coding: the era when AI goes from "assistant" to "teammate"
- Agentic Coding | Into the Data Data Science Wiki
- AI Agentic Coding
- What is agentic coding? How it works and use cases | Google Cloud
- Navigating Agentic Coding | HAHWUL
- Zed — Agentic Engineering
- A new skill for the AI agent era: what is Agentic Context Engineering?
9 survival skills for the age of agentic engineering
Decomposition
- Say "build a sign-up feature" and something does come out. The problem is that it is likely not what you wanted
- Experience of handing the AddPlan screen to an agent with just a PRD, then losing half a day to dozens of turns of back-and-forth
- A 5-minute Socratic interview with the AI → edge cases sorted out in advance → revisions cut to 2-3 turns
- Taking time to think before implementing — those 5 minutes save 4 hours
Context Architecture
- Writing AGENTS.md well matters, but if the code architecture itself is well designed, the speed at which the agent grasps the context is completely different
- The agent was lost in a flat directory; restructuring into per-Feature directories brought immediate improvement
- Armin Ronacher: "Tools need to be prepared for the LLM chaos monkey using them completely wrong"
Definition of Done
- Left a CLI project running overnight and it finished in 1 hour — only the type definitions were set up, and the business logic was an empty shell
- On the second attempt, the agent rewrote the tests themselves to suit itself
- The agent's "done" is different from my "done"
- Elvis's 7-step DoD system (PR→CI→3 code reviews→Telegram) is extreme but shows the direction
Failure Recovery Loop
- In a redistribution engine, the same parameter had different semantics in each function → an infinite loop where fixing A breaks B
- Retrying with the same prompt is banging your head against the wall in the same direction over and over
- Classifying failures into 3 types (lack of context, wrong direction, structural conflict) makes the remedy clear
- A "Must NOT Have" guardrail broke the infinite loop
Observability
- Handing liquidglass to an agent and thinking "that's odd... let it be" was the most expensive decision
- 20 files tangled together, impossible to roll back
- Afterwards, a tracer bullet strategy + blueprint — you cannot draw a blueprint in advance for a technology you are applying for the first time, so tracer bullets sketch it quickly
- Observability builds trust, and trust makes delegation possible
Memory Architecture
- Working 3 days in a row, you lose 15 minutes every morning explaining the context
- Automatic memory extraction at session end with Claude Code hooks → 5-second restore in the next session
- Boris Cherny's team checks CLAUDE.md into git so the whole team shares it
- A structure where the team's memory, not an individual's, is passed to the agent
Parallel Orchestration
- Boris Cherny runs 10-15 parallel sessions at once
- The experience of managing 6 squads as a CTO is surprisingly similar to managing agents in parallel
- Not ADHD but intentional multitasking = managing
- People ask questions, but agents proceed on their own judgment without asking — up-front design matters more
Abstraction Layering
- Level 0 (coding directly) → Level 1 (instructing agents) → Level 2 (orchestrator) → Level 3 (meta design)
- Experience of turning a 20-minute daily routine into a skill and cutting it to 2 minutes
- Compounding engineering — a project is not a finish-line game but a compounding game. Earlier sessions compound into later ones
Taste
- AI-made designs score 60-70 points. The moment Ellie's design comes in, there's a sense of "ah, this works"
- An AI-made informational post got 0 likes, while an impulsive one-line brag post got 30,000 views
- KinglyCrow's "No Skill, No Taste" — LLMs lowered the skill barrier to entry, but the real barrier, taste, is if anything amplified
- Chris Lattner: "The more implementation is automated, the more design, judgment, and taste matter"
- In an era flooded with the 80%, differentiation comes from the remaining 20%
Closing
- What ended is typing, not engineering
- These 9 are what good engineers had even before AI
- The leverage of good design has grown, but so has the damage of bad design
- The star of the show is not AI but the engineer who handles AI well
See also
Favorite site
Pages that link here