How Do Startup CTOs Build a Software Factory with AI Agents?

Why Does Running Multiple AI Coding Agents Still Feel Like Babysitting?

You have Claude Code or Cursor running. Maybe multiple instances. But you are still the orchestrator — reviewing each output, deciding what happens next, copy-pasting context between sessions. You are running parallel agents, not a software factory.

A software factory requires incrementally removing you from the loop. Work flows from spec to production autonomously, with you on-the-loop (able to see state and intervene) but not in-the-loop (not driving each step). The difference is structural, not just about adding more agents.

The Software Factory Primitives Framework diagnoses exactly what is missing. Audit your four primitives: Runtime (where agents run — probably solved), Orchestration (can you scale agents — probably solved), Triggers (do events start agents — partially solved), Coordination (can agents gate progress and hand off — almost certainly missing).

How Do I Go From Spec to Reviewable PR Without Constant Intervention?

The Swarm pattern fits single-repo work: one intent (a spec) fans out to multiple sub-agents that funnel results back into a single PR. But swarm without coordination is chaos.

Decompose your SDLC into micro-steps:

Plan micro-steps:

1. Parse the spec

2. Identify affected components

3. Generate task list with dependencies

4. Validate task list against codebase structure — gate

Build micro-steps:

5. Scaffold new files

6. Implement component A

7. Implement component B

8. Integration pass — gate: code compiles

Test micro-steps:

9. Write unit tests for component A

10. Write unit tests for component B

11. Run all tests — gate: tests pass, coverage threshold met

12. Edge case tests — gate

Review micro-steps:

13. Self-review against spec

14. Raise PR with structured description

Build a CLI gateway — a tool your agent can invoke as a tool call: "Have I completed micro-step 8? May I proceed?" The gateway checks machine-verifiable conditions (compiles, tests pass, files exist) and returns a proceed/halt signal. This prevents agents from claiming completion when they have skipped steps.

What Is Context Rot and Why Does It Kill Long-Running Agent Tasks?

Context rot is the degradation of agent performance as the context window fills. On a 30-minute task, the agent starts strong but gradually loses track of the spec, skips steps, and produces lower-quality output. This is the hardest problem in building a software factory.

The fix is Harness Engineering: after each failed run, identify exactly which micro-step the agent drifted at. Encode the fix back into the repository:

- agents.md: process rules, coding standards, SDLC step sequence

- Context files: architecture decisions, component relationships, spec details

- Unit tests: machine-checkable verification that catches agent drift

The repository becomes smarter over time. Each Harness Engineering cycle makes agents more reliable without you needing to babysit them.

How Do I Know When to Intervene vs When to Let the Agent Continue?

Design your oversight UX to show where to intervene, not everything that is happening. Your coordination layer should surface:

- Which micro-step each sub-agent is on

- Which gates have passed or failed

- Where an agent is blocked or looping

Do not pipe all agent activity through GitHub PRs or Linear tickets. You will drown in noise. Build a lightweight dashboard or CLI status command that shows coordination state.

Next step: Pick one feature spec and decompose its SDLC into micro-steps. Build a simple CLI gateway that checks one gate (tests pass) before the agent proceeds. Run one Harness Engineering cycle. This gives you the foundation to expand incrementally toward a true software factory.