Agentic Full-Stack Development: Build the Feedback Loop

Agentic development works when code changes are tied to visible results.

0. TL;DR

Agentic development becomes useful when the agent can do more than generate code. It needs to run the code, inspect what happened, and keep iterating until it can show evidence that the change worked.

The difference is not better prompting. The difference is a working feedback loop.

1. What Agentic Full-Stack Development Means

In practice, this is not "AI writes code." It is "AI executes a complete development loop":

1. Make a small change.
2. Run tests, scripts, or the app.
3. Inspect outputs and intermediate state.
4. Decide the next step from evidence.
5. Repeat until there is a clear pass condition.

This works with one agent or multiple agents. You can have one agent implementing and another verifying. The important part is that each step is driven by observed results, not guesses.

2. Why Feedback Changes Everything

Code generation alone is helpful for quick starts. It can scaffold files, suggest patterns, and speed up routine edits.

But most real bugs are not solved at generation time. They are solved by observing behavior:

• A test fails for a specific reason.
• A network request returns the wrong payload.
• A query writes incorrect intermediate data.
• A deployment uses a different config than expected.

Without feedback, an agent can only produce plausible text. With feedback, it can produce working changes.

3. The Development Loop You Can Copy

Use this loop as the default:

1. Plan the smallest useful change.
2. Implement it.
3. Run the relevant checks.
4. Observe outputs and state.
5. Record a verdict: pass or fail, plus why.
6. Iterate or stop.
7. Package evidence.

Three habits make this loop reliable:

• Keep diffs small.
• Keep pass criteria explicit.
• Keep iteration fast.

When these are true, progress is easy to measure and easy to trust.

4. Treat Evidence Bundles as a Required Deliverable

An evidence bundle is the output that proves the change is real. It should be part of the workflow, not an afterthought.

A simple checklist:

• Patch or commit reference.
• Exact commands that were run.
• Test or script results with exit codes.
• Key logs or output excerpts.
• Artifacts (screenshots, traces, JSON output, benchmark results when needed).
• Short explanation of what changed and why.
• Short explanation of how the evidence supports success.

You can store this in a markdown file under /evidence, as a PR comment template, or as CI artifacts. The location matters less than consistency.

5. Make the Application Observable to the Agent

Generated from the Section 5 prompt.

Agents need handles into reality. In most projects, this is where progress slows down for both teams and solo builders.

5.1 Execution Observability

Standard scripts should exist and be predictable:

• dev
• test
• lint
• typecheck
• e2e (if applicable)

One obvious command per task is a contract. It removes guesswork.

5.2 Output Observability

Outputs should be stable and machine-friendly:

• Consistent error summaries.
• Structured lint and test output.
• Captured exit codes.
• Short summary lines that are easy to parse.

If output shape changes every run, the loop becomes fragile.

5.3 Intermediate State Observability

For data-heavy flows, inspect intermediate state directly:

• Tables and row counts.
• Null and uniqueness checks.
• Generated files.
• Queue or cache state when relevant.

This is often where hidden issues are found fastest.

6. Tool Pillars That Improve Feedback Quality

6.1 Browser Verification (Chrome MCP)

Useful for reproducing UI bugs and validating user flows:

1. Reproduce issue.
2. Inspect console and network.
3. Patch.
4. Re-run and confirm behavior.
5. Capture screenshot and key logs.

Evidence typically includes console output, network snippets, and final screenshots.

6.2 Infrastructure Reality Checks (AWS CLI)

Useful for validating deployed state:

1. Verify identity and region.
2. Read actual resource configuration.
3. Compare expected vs actual fields.
4. Change only after read-side validation.

Evidence should include exact commands and redacted JSON excerpts of real state.

6.3 Local Databases

Useful for checking data correctness during development:

1. Write intermediate outputs to explicit tables.
2. Run basic invariants after each step.
3. Save query outputs that confirm correctness.

Evidence includes executed queries and the resulting counts or sample rows.

6.4 CI/CD

Useful as an external judge:

1. Push branch.
2. Read failing jobs.
3. Fix and rerun until green.
4. Attach CI evidence.

Evidence includes CI links, fail-to-pass summaries, and test artifacts.

6.5 Git

Useful for change clarity and rollback safety:

• One logical change per commit.
• Use git diff as source of truth for what changed.
• Reference evidence in commit or PR text.

This keeps reviews clear and debugging faster.

7. Why Monorepos Help Agentic Work

Agentic workflows improve when the codebase is visible in one workspace:

• Frontend, backend, and infrastructure in one tree.
• Shared types reduce interface mismatch.
• Consistent scripts reduce command ambiguity.

A simple structure:

/apps/web
/apps/api
/infra/cdk
/packages/shared

When root and package scripts use the same names (dev, test, lint, typecheck), agents can navigate the project with less trial and error.

8. Guardrails That Keep Verification Cheap

Use guardrails that reduce friction:

• Fast smoke tests for quick signal.
• Stable repro commands.
• Fewer flaky checks.
• Data constraints and API contract checks.
• "No console errors" checks for important UI flows.
• Structured logs with consistent keys.

A simple rule helps a lot: every final answer must cite evidence, not just conclusions.

9. Keep Safety Simple

Use least-privilege credentials, keep tool-call logs, and separate read from write access where possible. Keep this lightweight but non-optional.

10. Closing: Start With One Loop

The goal is not full autonomy. The goal is reliable competence.

If you are adopting this now, start with three steps:

1. Standardize scripts.
2. Require evidence bundles.
3. Add one strong verification surface (browser checks or database checks).

Then expand into CI and infrastructure validation as your workflow matures.