motionwiki / operating layer for ai buildersThe five steps

most agent builds fail before the first prompt

not a model problem. five things break. all fixable.

latest field note

how i moved my always-on agent from claude code to hermes + gcp

read the setup →
01

Step 01 / plan

“help me build this” is not planning.

Before prompting, map four layers: the subject domain, the tech stack, current best practices, and exactly where this hooks into what already exists.

Subject domain

A finance agent is not a fitness agent. Study the domain, not just the idea.

Tech stack

Know the SDKs, APIs, and libraries the build will actually depend on.

Current best practices

Providers update SDKs monthly. Mine them fresh every build.

Integration points

Name the exact seams where the new work connects to what exists.

Agent loops have their own layer on top: tool usage, memory, system prompts, and loop logic. All have modern standards, and all shift fast. Mine them fresh every build.

02

Step 02 / prune

context rot

Exploration produces 10x more ideas than v1 needs. The agent suggests “we should also refactor x.” You accept. Again. Scope balloons until nothing ships coherently.

Two filters on every generated idea

01 / valuable?

If no, discard.

02 / needed now?

If no, straight to backlog. Background loops prioritize later.

I cut exploration output by 80% and failed builds went to near zero. The point is not to stop exploring. It is to stop letting every exploration result become a commitment.

03

Step 03 / build

the build layer

Skills alone are not guardrails. Run builds through a spec driven SDLC: proposal (the why), design (the what), specs (acceptance criteria), tasks (the how).

01

Proposal

the why

02

Design

the what

03

Specs

acceptance criteria

04

Tasks

the how

  • never dump the entire spec and say "build this"
  • break into small contained tasks, one feature at a time
  • never let the build start without acceptance criteria written first
  • when a run drifts at hour 3, the proposal doc snaps it back

A 15 hour autonomous run is only gradeable against criteria that existed before it ran. No re-explaining.

Tooling / spec system

OpenSpec if you ship fast solo. Spec Kit when there is a team or hard principles involved.

why → what → criteria → how
04

Step 04 / secure

the security layer

Agents hold keys, tokens, and data access. An unreviewed autonomous run with write access is an incident waiting to happen.

  • Least privilege

    Scope agent credentials to exactly what the task needs. Read only by default; write access is granted per task and revoked after.

  • Secrets hygiene

    Keys live in env vars or a vault, never in prompts, context files, or memory. Anything in a prompt can leak into logs and outputs.

  • Gate the dangerous actions

    Deploys, payments, deletions, and external sends always require human approval.

  • Sandbox the execution

    Runs happen in containers or scoped environments, not your host machine with full access.

  • Audit trail

    Log every tool call and file write so any drift is traceable after the fact.

  • Review before merge

    Run an adversarial review pass on every autonomous diff. Treat agent code like a junior dev's PR, not shipped truth.

05

Step 05 / sync

sync the loop

Sync specs back so future builds never contradict what works. Boring process. Compounding output.

planprunebuildsecuresync

Explore the domain, tech, best practices, and integration points. Prune noise and backlog the later stuff. Build through spec driven tasks, one feature at a time. Secure the run with least privilege, gated actions, and reviewed diffs. Sync the working specs back into the system.

generic input gets generic agents. dialed input gets systems.