AI agents explained

An AI agent—in the sense PunkSolid uses for AI agents explained—is software that calls a language model to decide what to do next in a repeating loop. The loop may read files, invoke a shell, call an HTTP tool through MCP, or apply patches in an IDE. It is not the same as a static CI script with frozen steps, and it is not the same as a marketing chat bubble that only returns text.

This page defines the loop, shows where agents fail without discipline, and separates engineering agents from consumer site builders elsewhere in our network.

The core loop

A practical decomposition matching how we deliver client work:

1. Observe — Gather state the policy allows: git status, ticket text, logs, failing test output, selected files.
2. Plan — Turn the goal into steps. Good plans come from a human-written spec, not from the model improvising a roadmap.
3. Act — Edit code, run commands, comment on a PR, or call a narrow MCP tool.
4. Verify — Tests, lint, diff review, content validators, or explicit human approval.

Step four is non-negotiable on production paths. Anthropic’s public guidance on building effective agents stresses bounded tasks and checkpoints; PunkSolid operationalizes that through methodology and spec-driven development.

  ┌──────────┐     ┌──────────┐     ┌──────────┐     ┌──────────┐
  │ Observe  │ ──► │  Plan    │ ──► │   Act    │ ──► │ Verify   │
  └──────────┘     └──────────┘     └──────────┘     └────┬─────┘
       ▲                                                   │
       └───────────────────── retry or stop ◄──────────────┘

How agents differ from automation scripts

Fixed automation	AI agent loop
Same steps every run	Next step depends on model + observations
Predictable failure modes	Can hallucinate APIs or paths
Great for deploy rails	Great for ambiguous refactors with specs
Owned by platform team	Needs repo rules (AGENTS.md, Cursor rules)

Use scripts for known rails; use agents for bounded creative work where human review still gates merge.

Coding agents vs other agent shapes

Coding agents (IDE or terminal) act directly on repositories. PunkSolid compares categories in AI coding agents compared and Claude Code vs Cursor in Claude Code vs Cursor.

Other agent shapes—support triage, sales email draft, browser shopping—share the loop but differ in risk surface. This site focuses on your git repo and deploy pipeline.

Tool access and MCP

Agents become dangerous when tools are wide. Prefer:

• Read-only observations first.
• Write tools listed in the spec.
• MCP servers with narrow APIs instead of unconstrained shell—What is MCP?.

AGENTS.md should name verify commands; Cursor rules should encode git and secret policy.

Failure modes we design around

Hallucinated APIs — The agent imports libraries or env vars that do not exist. Mitigation: typecheck and tests in acceptance; reject “looks fine” summaries.

Scope creep — Touching unrelated packages while “cleaning up.” Mitigation: out-of-scope section in every spec; path allowlists for large repos.

Silent risk — Secrets in commits, destructive rm, license violations from pasted Stack Overflow. Mitigation: deny lists, secret scanning, human review on auth and payments.

False verification — The agent claims tests passed without running them. Mitigation: CI as source of truth; paste command output in PR description.

SEO and migration damage — Redirect tables edited mid-migration, duplicate canonicals. Mitigation: signed URL inventories; validators in pnpm validate patterns.

Rescue engagements—AI code review—often start because verify was skipped, not because the model was “bad.”

Agents in multi-vendor teams

Cursor-first engineers and Claude Code terminal users can share one branch if they share one spec and one verify command. Mixed tooling does not relax compliance: both can exfiltrate secrets if rules are weak.

See Claude Code tutorial for terminal onboarding and agents-md-guide for repo-wide norms.

Not the same as “agentic website builders”

Consumer products that host pages on vendor infrastructure target a different buyer: fast publish, not repository ownership. PunkSolid’s agents aim at code you control—Astro, Next, Medusa, static networks—with code ownership as the outcome.

Sibling sites in the PunkSolid network cover agentic website keywords; this page stays on engineering agents.

Agent loops on content and commerce sites

Agents draft Markdown, seed CMS records, and refactor templates. Our own apps/ai-network build uses the same quality gate agents must respect: word minimums, FAQs, sources before reviewed. The PunkSolid network build case study describes that pattern for leadership conversations.

Ecommerce adds checkout, feeds, and merchant policy—agents do not ship pricing changes without human sign-off. See 48V Products ecommerce rebuild for migration discipline at commerce scale.

Organizational roles

Role	Agent-friendly work	Keep human-only
Engineer	Implement spec slices, tests, refactors	Architecture bets, threat modeling
Content	Draft guides with source lists	Regulated medical/financial copy
Ops	Read-only diagnostics via MCP	Production DNS, certificate cutovers
Legal	—	Tooling DPAs, client data flow

Starting an agent program

1. Pick verify commands that mirror CI.
2. Write AGENTS.md and minimal Cursor rules.
3. Pilot on one vertical slice with a written spec.
4. Measure failures (reverts, CI red) not vanity “lines generated.”
5. Expand tool access only with security review.

Where to read next

• Vocabulary: What is agentic AI?
• Tooling comparison: AI coding agents compared
• Delivery help: website development · contact

Stopping conditions and budgets

Every agent loop needs an explicit stop: tests passed, step count exceeded, tool error, or human interrupt. Without a stop, terminal agents especially can churn—re-running formatters, bumping lockfiles, or “trying one more approach.” Specs should name the maximum scope (files, lines, packages) and the verify command that ends the session.

Human interrupt is a feature. Reviewers who reject a diff should update the spec or out-of-scope list before restarting the agent—otherwise the second run often repeats the first mistake with different wording.

Teaching reviewers to evaluate agent output

Train reviewers to ask three questions before LGTM: Does the diff match the spec problem? Did CI run the acceptance commands? Are regulated strings untouched? Authorship (human vs agent) is irrelevant when those three are satisfied.

For mixed Cursor and Claude Code teams, the PR description should cite which tool touched which paths when policies differ—IDE-only templates vs terminal-only batch jobs. That reduces accidental policy violations during parallel work.