From Hooks to Agents: Four Layers of SaaS Automation
Not every task needs an autonomous agent. Learn the four-layer automation hierarchy — hooks, skills, scripts, and agents — that runs a full SaaS operation.
Everyone wants to talk about AI agents. The word "agentic" has become the fastest way to add a zero to your startup's valuation. But here's the thing most automation discussions get wrong: the majority of tasks in a real business don't need an agent at all. They need a hook. Or a script. Or a skill. Using an autonomous agent to validate a JSON file is like hiring a management consultant to check your spelling.
An agentic business doesn't throw agents at everything. It uses a hierarchy — four distinct layers of automation, each matched to the complexity of the task. Get the matching wrong, and you either over-engineer simple problems or under-automate the hard ones. Both waste money. Both slow you down.
This is how MyWritingTwin.com runs its entire operation with 2 hooks, 14 skills, 47 scripts, and 7 agents — and why the layering matters more than any individual tool.
The Over-Automation Trap
The instinct, when you first discover AI agents, is to automate everything with the most powerful tool available. Need to check if a file exists? Agent. Need to format a date string? Agent. Need to send a notification? Agent.
This is the equivalent of driving a forklift to the corner store for milk. It works, technically. But you've introduced latency, cost, unpredictability, and failure modes that a simpler solution avoids entirely.
The real cost isn't just the API tokens. It's the debugging surface. An autonomous agent that makes decisions introduces decision points that can go wrong. A pre-commit hook that validates translation keys either passes or fails — there's no "judgment" involved, no prompt that can be misinterpreted, no chain-of-thought that veers off course. It's deterministic. That's a feature.
Most businesses building with AI make the opposite mistake too. They automate the trivial stuff — chatbots for FAQ pages, AI-generated social media captions — while leaving genuinely complex workflows entirely manual. The content strategist still spends 45 minutes assembling a briefing doc from six different sources. The QA process still requires a human to remember all seven checks and run them in the right order. The analytics review still means logging into five dashboards and cross-referencing numbers by hand.
The four-layer architecture solves both problems. Simple tasks get simple automation. Complex tasks get the autonomy they need. Nothing more, nothing less.
Layer 1: Hooks — Zero-Effort Guardrails
Hooks are the foundation. They run automatically, without human invocation, and they enforce standards that should never be violated.
What Hooks Do
A hook triggers on a specific event — a git commit, a file save, a deployment — and runs a validation check. It either passes silently or blocks the action with an error. There's no AI involved. No language model. No tokens spent. Just deterministic logic.
Real Examples
MyWritingTwin uses 2 pre-commit hooks that prevent 100% of a specific class of failure:
Translation sync validation. The app supports four languages — English, Japanese, French, and Spanish. Every UI string in messages/en.json must have a corresponding key in messages/ja.json, messages/fr.json, and messages/es.json. Before this hook existed, the team would occasionally ship a new feature with English strings that had no Japanese translation. The result: broken UI for Japanese users, caught only after deployment.
The hook checks every translation file for key parity on every commit. If a key is missing, the commit is rejected. Time to implement: about an hour. Failures prevented since: every single one. The error rate for translation sync issues went from "occasional but embarrassing" to zero.
Frontmatter validation. Every blog post requires specific metadata — title, description, publishedAt date, tags. Missing any of these breaks the sitemap, the RSS feed, or the SEO metadata. The hook validates frontmatter structure before any content file can be committed.
When to Use Hooks
Hooks are right for tasks that meet three criteria:
- Binary outcome. Pass or fail. No gray area, no judgment needed.
- High frequency. The event they guard happens often — every commit, every deploy, every publish.
- Catastrophic if missed. The failure they prevent is worse than the minor friction of the check.
If a task requires interpretation, decision-making, or context awareness, it's not a hook. It's something higher in the hierarchy.
Layer 2: Skills — One Command, One Outcome
Skills sit between hooks and scripts. They're user-invoked — a human decides when to run them — but the execution is handled by AI. One command in, one structured result out.
What Skills Do
A skill encapsulates a multi-step workflow that previously required manual effort across multiple tools. The human provides the trigger and the parameters. The skill handles research, synthesis, formatting, and delivery.
Real Examples
MyWritingTwin runs 14 skills. Here are three that illustrate the pattern:
Content briefing. Input: a blog topic and target keyword. The skill reads internal documentation, scans existing blog posts for overlap, pulls positioning guidelines from the brand voice docs, checks terminology rules, identifies internal linking opportunities, and returns a structured brief. Manual version: open six tabs, read through docs, cross-reference existing content, write up a brief. Time: 30-60 minutes. Skill version: one command, same output, under 30 seconds.
Competitive research. Input: a competitor URL or product name. The skill analyzes their positioning, pricing, feature set, content strategy, and identifies gaps or differentiation opportunities. Returns a structured analysis with specific recommendations. Manual version: hours of browsing, note-taking, and synthesis.
Image generation. Input: a blog post file path. The skill reads the post content, determines the visual concept, generates a hero image following brand guidelines, and saves it to the correct directory with the correct naming convention. No context-switching to a separate image tool, no manual prompt engineering, no file management.
The 30-60 Minute Rule
A useful heuristic: if a manual task takes 30-60 minutes and follows a repeatable pattern, it's a candidate for a skill. Below 30 minutes, the overhead of building the skill may not pay off. Above 60 minutes, the task likely involves enough decision-making to warrant a script or agent.
The 14 skills in the MyWritingTwin stack compress roughly 14-20 hours of weekly manual work into minutes of invocation time. That's not a marginal improvement. That's the difference between having bandwidth for strategy and drowning in execution.
Layer 3: Scripts — Operational Plumbing
Scripts are the workhorse layer. They handle specific operational tasks with direct parameters — no AI reasoning required, but more complex than a hook and more targeted than a skill.
What Scripts Do
Scripts automate repetitive operational work. They take explicit inputs, perform a defined sequence of operations, and produce a predictable output. They don't make decisions. They don't interpret ambiguous instructions. They execute.
Real Examples
MyWritingTwin operates 47 scripts across several categories:
Content operations. Translation sync scripts that propagate content across four locales. Blog post scaffolding scripts that create files with correct directory structure and frontmatter templates. Social media distribution scripts that format posts for different platforms and push drafts to scheduling tools.
Video production. Audio generation scripts for video narration. Scene assembly scripts for video editing pipelines. Subtitle generation and timing scripts.
Analytics and reporting. The admin stats script pulls data from PostHog, Google Search Console, YouTube Data API, and Anthropic's usage API. It aggregates metrics into a single dashboard view — pageviews, search queries, API costs, conversion events. Running this manually means logging into five separate platforms and manually cross-referencing data. The script does it in one command.
Infrastructure. Database migration runners. Environment validation scripts that verify all required API keys are configured before deployment. Sitemap generators. RSS feed builders.
Scripts vs. Skills
The distinction matters. A skill involves AI reasoning — it reads context, makes choices about what to include, synthesizes information. A script doesn't reason. It follows a fixed procedure with variable inputs.
Translation sync is a script: take English content, copy structure to other locale directories, flag strings that need human translation. No interpretation needed.
Content briefing is a skill: read brand docs, analyze existing content for overlap, determine the right angle based on what already exists. Interpretation is the whole point.
When teams blur this line — using AI reasoning for tasks that don't need it — they introduce unnecessary cost, latency, and unpredictability. A script that syncs files should not be calling a language model. It should be copying files.
Layer 4: Agents — Autonomous Orchestration
Agents are the top of the hierarchy. They're the layer most people think of when they hear "AI automation" — and the layer that should be used least often.
What Agents Do
An agent takes a goal, not a procedure. It plans the steps, executes them in sequence, makes decisions at each stage, handles errors, and delivers a finished result for human review. Unlike a script, an agent adapts. Unlike a skill, an agent chains multiple operations with branching logic between them.
Real Examples
MyWritingTwin runs 7 standing agents. Here's how two of them work in practice:
Quality Gate agent. Goal: validate the codebase for production readiness. The agent runs seven checks in sequence — TypeScript type checking, full test suite (930+ tests), production build verification, translation file integrity, broken link detection, SEO validation, and secret scanning. But it doesn't just run the checks. It analyzes failures, classifies them by severity, determines whether a failure is a blocker or a known issue, and produces a structured report with recommendations.
Manual QA of the same scope takes roughly 2 hours — running each check, reading output, cross-referencing known issues, writing up findings. The Quality Gate agent does it in about 5 minutes. Same coverage. Same judgment about severity. Two orders of magnitude faster.
Content Pipeline agent. Goal: produce a publication-ready blog post on a given topic. The agent runs a content briefing (skill), checks for existing content overlap, adjusts the angle based on what it finds, writes the post following brand voice guidelines and style extraction methodology, generates a hero image, verifies frontmatter and SEO elements, creates social media drafts, and reports a summary for final human review.
This is the agent that helped produce 161 blog posts across four languages. Not by generating filler content at scale — by following the same methodology, terminology rules, and positioning guidelines a human content strategist would use. Each post goes through the same quality checks. Each one respects the same brand rules.
The Judgment Threshold
Use an agent when the task requires genuine decision-making at multiple points during execution. If the task can be fully specified in advance — all inputs known, all steps predetermined, no branching logic — it's a script, not an agent.
The cost difference is real. An agent consumes significantly more API tokens than a script. It's slower. It can make wrong decisions that need correction. These tradeoffs are worth it when the alternative is a human spending hours on a multi-step process. They're not worth it when a deterministic script would produce the same result.
Matching Tasks to Layers
The architecture works because each layer handles what it's best at. Here's the decision framework:
| Question | If Yes | If No |
|---|---|---|
| Is the check binary (pass/fail) and triggered by an event? | Hook | Keep reading |
| Is it a single workflow with one input and one output? | Skill | Keep reading |
| Is it a repeatable operation with fixed steps? | Script | Keep reading |
| Does it require multi-step planning and decision-making? | Agent | Reconsider if automation is needed |
A common pattern: tasks start as manual processes, get automated as scripts once the procedure stabilizes, and some get promoted to agents when decision-making requirements emerge. The translation sync started as a manual checklist, became a script, and the validation piece became a hook. The content creation process started as a skill (briefing only), then expanded into a full agent as more steps were added.
The wrong move is jumping straight to agents for everything. Build from the bottom up. Hooks first. Then skills and scripts. Agents only when lower layers can't handle the complexity.
The Cost Arithmetic
Let's make the economics explicit.
Layer 1 — Hooks: Zero marginal cost. They run locally, no API calls, no tokens. The two pre-commit hooks cost nothing to operate after the initial hour of setup.
Layer 2 — Skills: Zero marginal cost. Skills run through the Claude Max flat-rate subscription. A content briefing, a competitive research skill, a code review — all covered by the same $200/month regardless of how many times they're invoked.
Layer 3 — Scripts: Mostly zero cost (local execution). The analytics dashboard script calls several external APIs but uses free tiers. Image generation scripts (Gemini, DALL-E) are the one exception — they incur small per-image API costs.
Layer 4 — Agents: Also zero marginal cost through the flat-rate subscription. A full Content Pipeline run, a Quality Gate validation, a Daily Briefing — all covered. The only agent operations with per-use costs are customer-facing ones (like Style Profile generation), which use separate API billing and are self-funded by customer payments.
The layering still matters for quality. Even though the flat-rate subscription removes per-operation cost pressure, routing deterministic tasks to hooks and scripts instead of agents produces better results — because agents introduce unnecessary variability for tasks that should be predictable.
Building Your Own Hierarchy
You don't need 47 scripts and 7 agents to start. You need the mental model.
Start with hooks. Identify the failures that keep recurring — the deployment that broke because someone forgot a step, the content that went live with missing metadata, the code that shipped with a hardcoded secret. Each recurring failure is a hook candidate.
Identify your 30-60 minute workflows. The tasks you do repeatedly that follow a pattern but require pulling information from multiple sources. These are your first skills.
Audit your operational checklists. Any checklist with more than five steps that you run regularly is a script candidate. If you're copy-pasting commands from a document, that document should be a script.
Reserve agents for genuine complexity. Multi-step processes where the right action at step 4 depends on what happened at step 2. Where branching logic and error handling require real judgment. Where the alternative is a human spending hours on orchestration.
The four-layer architecture isn't about having the most sophisticated AI. It's about using the right level of sophistication for each task. Hooks for guardrails. Skills for workflows. Scripts for operations. Agents for orchestration.
Most businesses over-automate simple tasks and under-automate complex ones. The hierarchy fixes both.
See Systematic AI Analysis in Practice
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