Agent-native systems
Agents act as users and operators inside bounded workflows, with audit trails and escalation paths.
AI systems practice
You can outsource your thinking, but you can't outsource your understanding.
Software stack
Every durable digital system eventually meets code.
Agents, models, products, and workflows become operational only when they land in systems that can be built, tested, versioned, reviewed, deployed, observed, and rolled back.
AI-assisted codebases
Models draft changes, tests and reviews constrain them, and prompts become versioned artifacts.
Learned behavior
Model behavior is learned from data, then governed through evals, monitoring, and correction.
Explicit program logic
Humans encode rules directly, review diffs, and remain accountable for production behavior.
Fundamentals
- Build
- Test
- Diff
- Review
- Deploy
- Observe
Base layer
Code
This is a working model, not a universal taxonomy. The important question keeps moving: who writes the code, who reviews the change, who owns the codebase, and who is accountable when the system changes.
Workflow fit
Each workflow requires a unique solution.
No Agent (Just Code)
Deterministic, repeatable work belongs in scripts, APIs, rules, and automations.
Single Agent / Sequential
One agent or a linear chain handles dependent steps: research, plan, implement, review.
Parallel Multi-Agent
Independent workstreams run at the same time with limited overlap and clear interfaces.
Supervised Agent Teams
Persistent teams operate with shared context, tools, guardrails, handoffs, and human escalation.
Agentic engineering
Access raises the floor. Engineering raises the ceiling.
The goal is higher throughput without letting quality drift. Agents execute implementation inside bounded scopes; humans own architecture, approval, quality thresholds, and correctness criteria.
Data
Systems
Workflows
-
01
Separate probabilistic reasoning from deterministic control paths.
-
02
Use fewer agents and more code when the work is repeatable.
-
03
Plan deeply, constrain execution, keep changes small, and treat prompts like code.
-
04
Maximize visibility with logs, reviews, evals, guardrails, and rollback paths.
Agentic use stack
The model is only one part of the system.
Reliable agentic work depends on the surrounding operating layer: standing instructions, reusable skills, controlled tools, live context, bounded permissions, orchestration, and verification.
Instructions
AGENTS.md sets standing rules, boundaries, tone, repository norms, and what done means.
Skills
Reusable procedures turn recurring work into known playbooks instead of fresh prompting every time.
Tools
Shell, browser, APIs, files, design systems, and apps give agents controlled ways to act in the world.
Context
Code, docs, task history, product rules, memory, and current state keep execution grounded.
Control Layer
Sandboxes, secrets, permissions, approvals, and data gates define what the system can touch.
Orchestration
Plans, queues, handoffs, parallel work, and escalation paths coordinate more than one agent or step.
Verification
Tests, diffs, logs, evals, reviews, and rollback paths decide whether the work is safe to ship.
Guardrailed workflows
Designed workflows stay useful when every handoff has a check.
Research, planning, implementation, and testing move through designed constraints: sandboxed execution, limited permissions, rule sets, data gates, and rollback paths.