Hire an AI software architect

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• A short problem statement with your limits written back to you
• A map of the main business areas and where the system splits
• At least two options, each with what it gives and what it costs
• A recommended design and the reasons for it
• A decision record with context, decision and consequences
• A growth path, so the system can change without a rewrite

How it does the job well

• No architecture astronautics — Every abstraction must justify its complexity
• Trade-offs over best practices — Name what you're giving up, not just what you're gaining
• Domain first, technology second — Understand the business problem before picking tools
• Reversibility matters — Prefer decisions that are easy to change over ones that are "optimal"
• Read the existing code, its conventions and its tests before changing anything. Match the project's patterns.
• Make the smallest change that fully solves the request. Keep unrelated refactors out.
• Reproduce a bug before fixing it, and add a test that fails without the fix.

Mistakes it avoids

• Claiming code works without running it or its tests.
• Inventing APIs, package versions or settings instead of checking the installed ones.
• Changing behaviour outside the request, such as reformatting whole files or renaming public interfaces.

What a good result looks like

An example, not your task: You are starting a product and must choose between one app, a modular app or separate services.

• Problem and limits. Your goal, team, limits and expected growth, written back so you can correct them.
• Business areas. The main areas of the system, what each owns and how they talk to each other.
• Options. At least two structures, each with what it gives, what it costs and when it fits.
• Recommendation. The chosen option, the reasons and what you give up by choosing it.
• Boundary rules. Which parts may depend on which, and why.
• Decision record. Context, decision and consequences, in a form your team can sign off.

How the work is checked

• The changed files build, and the relevant tests and linters pass. The commands and results are shown.
• Each change is explained in one line with its reason, and risky areas are named.
• Any check that could not run is reported as NOT_RUN with what it needs.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

A second opinion

Before it hands back, your AI reviews the draft the way Margaret Hamilton would. This is a way of reviewing the work. It does not mean they wrote or endorse it.

Capabilities

• State the problem and limits. It restates your goal and limits first and checks that it has them right.
• Map the business areas. It finds the main areas of the business and how they relate, and says if heavy domain design is worth it.
• Compare patterns. It weighs layered, modular monolith, microservices, event-driven and other patterns for your case.
• Check the qualities. It looks at scale, failures, upkeep and how you will see problems.
• Set boundary rules. It sets which parts may depend on which, so core rules do not depend on tools or databases.
• Record the decision. It writes the decision record and the path for how the system can grow.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id engineering-software-architect only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/engineering-software-architect/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• The goal of the system and who uses it
• Limits, such as team size, budget, deadlines and rules you must follow
• Your current stack and what hurts today (optional if new)
• What you expect to grow, such as users, data or features
• The decision you must make, if you have one (optional)
• A description of your code layout or a key folder tree (optional)

Helpful context (optional)

• A list of the changes that were hardest to make in the last year.
• Your expected users and data size in a year, even as a rough range.

Tools and access

• Claude, ChatGPT or Gemini: A chat is enough for the proposal. A tool that reads your project files can check the plan against your real code.

Functions and deliverables

• Software architect: Choose how to structure your software, with at least two options, their trade-offs, a recommended design and a written decision record.
  • A short problem statement with your limits written back to you
  • A map of the main business areas and where the system splits
  • At least two options, each with what it gives and what it costs
  • A recommended design and the reasons for it
  • A decision record with context, decision and consequences
  • A growth path, so the system can change without a rewrite
  Source: Michael Sitarzewski and Agency Agents contributors.
• Decision record writer: Write a decision record for a design or tech choice you made. You get the reasons, the options you passed on and the trade-offs, in a short file.
  • Decision record in your project's format
  • List of rejected options with reasons
  • Stated trade-offs
  • Suggested file name and place
  Source: Addy Osmani.
• Trade-off option modeller: Set goals and limits. Get non-dominated options from a Python optimizer to compare.
  • A Python script that runs the optimization
  • A table of the best trade-off solutions
  • A chart of the trade-off curve
  • A test-problem check with a one-line verdict
  • A note on which solution suits which priority
  Source: K-Dense.
• Project design and decision recorder: Work on a codebase. Get notes on its design, decisions and coding rules for later AI work.
  • A notes folder of architecture, decisions and conventions
  • Updated notes after each change
  • Audit of stale or missing entries
  • Compact versions for quick loading
  Source: sickn33.
• AI project context organiser: Share your project and next task. Get a short rules file, a focused file list and a clear handoff for later sessions.
  • A ready rules file for your project
  • A list of which files to load for each task
  • Rules for when to reset or trim the session
  • A checklist for spotting drifting answers
  Source: Addy Osmani.
• Margaret Hamilton: Review a system failure plan using Margaret Hamilton’s recorded approach. Check handoffs, priorities, warnings and what happens when people make mistakes.
  • Your problem worked on the way Margaret Hamilton would: their questions, their method, their push-back
  • The answer or the work, in Margaret Hamilton's voice, using their ideas by name
  • Anything that goes beyond their public record, marked
  • One suggested next step

Tasks you can use it for

• You are starting a product and must choose between one app, a modular app or separate services.
• Your codebase is tangled and every change breaks something far away.
• A team argues about a technical choice and you need the trade-offs written down.
• A new feature, such as payments or a partner link, needs a clear boundary around it.

Limits to check before starting

• You need an API and data layout for one backend. Use the Backend Architect agent.
• You want a bug fixed or a pull request reviewed. Use the Code Reviewer agent or the Focused code fix developer.
• You want a threat review of a design. Use the Security Architect agent.
• You want a quick test of an idea. Use the Software prototype designer.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Download the pack from this page and add it to your AI as a skill. If you cannot, paste SKILL.md into a new chat, then references/source-agent.md.
• Describe the goal, the team, the stack and what will grow. Say what decision you need to make.
• Say "Write the architecture proposal. Ask me once if anything is missing."
• Read the options section first. Check each option's costs against what your team can actually run.
• Challenge the recommendation with the agent's own question, "What happens when this part fails?" Ask it to answer.
• Save the decision record in your project folder, and share it with your team before you build.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/engineering-software-architect for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.