Agentic Software Engineering in Go: Introduction
Beyond unconstrained generation: building reliable AI-assisted development systems
The Problem: Vibe Coding Doesn’t Scale
We’ve all seen it. An LLM generates impressive code in seconds. It compiles. It even runs. But three weeks later:
- The “quick prototype” is in production
- No tests exist for the generated paths
- The original prompt is lost to history
- No one knows why certain decisions were made
- Technical debt compounds silently
This is vibe coding — treating AI as a magic code generator rather than an engineering tool. It works for throwaway scripts. It fails catastrophically for production systems.
The Solution: Agentic Software Engineering
Agentic Software Engineering applies rigorous engineering discipline to AI-assisted development:
| Vibe Coding | Agentic Engineering |
|---|---|
| Prompt → Code | Spec → Context → Plan → Code → Verify |
| Hope it works | Prove it works |
| No memory | Persistent context & constraints |
| Single shot | Verification loops with feedback |
| Black box | Auditable, reproducible process |
Core Principles
1. Context Engineering
The agent’s output quality is determined by the quality of context you provide. Architectural Decision Records (ADRs), specifications, and code patterns become first-class engineering artifacts that constrain the agent to your standards.
2. Specification Engineering
Replace ambiguous English prompts with precise, machine-readable specifications. Define intent, constraints, inputs, outputs, and executable steps — then validate the spec before the agent ever runs.
3. Verification Loops (AI-TDD)
Every agent-generated artifact passes through automated verification: build, test, lint, and constraint validation. Failures feed back to the agent for correction — automatically, up to a retry limit.
4. Autonomy Calibration
Not all tasks deserve the same autonomy. Calibrate agent freedom based on risk, context quality, and historical success using the AEMM maturity model and Elastic Loop framework.
5. Security by Design
Prompt injection defense, sandboxed execution, and role-based access control are not afterthoughts — they’re built into the harness.
The Go Advantage
These patterns are technology-agnostic. But implementing them in Go provides:
- Static guarantees: Types catch errors before the agent even runs
- Native concurrency: Worker pools and orchestration map naturally to goroutines
- Production readiness: Single binary deployment, no runtime dependencies
- Ecosystem integration: Works with your existing Go tooling and CI/CD
This Series
Over 12 posts, we’ll build a complete Agentic Software Factory in Go:
| Post | Topic | Code |
|---|---|---|
| 1 | Context Engineering — ADRs, context curation, open vs closed loop | pkg/context |
| 2 | Specification Engineering — Machine-readable specs, Plan-Build-Verify | pkg/spec |
| 3 | AI-TDD Verification Loops — Build, test, lint, constraint validation | pkg/verify |
| 4 | Complete Harness — Integrated context + specs + verification | pkg/harness |
| 5 | Autonomy Calibration — AEMM, Elastic Loop, decision gates | examples/chapter7 |
| 6 | Security & Safety — Prompt injection, sandboxing, RBAC | pkg/security |
| 7 | Team Integration — Git hooks, PR review, shared context | examples/chapter9 |
| 8 | Developer Workflow — Spec authoring, daily loop, prompt patterns | examples/chapter10 |
| 9 | Software Factory Orchestration — Orchestrator-Worker-Validator | pkg/factory |
| 10 | Production Operations — Observability, metrics, improvement | cmd/asf |
| 11 | Case Study — End-to-end feature from spec to production | examples/ |
| 12 | Future Directions — Self-improving factories, model routing | — |
Repository
All code lives in agentic-software-factory-go — a private repository with:
agentic-software-factory-go/
├── pkg/
│ ├── context/ # Context curation engine
│ ├── spec/ # Specification parser & validator
│ ├── verify/ # AI-TDD verification engine
│ ├── harness/ # Complete integrated harness
│ ├── security/ # Sandbox, RBAC, injection defense
│ └── factory/ # Orchestrator-Worker-Validator
├── cmd/asf/ # CLI for daily workflow
├── examples/ # Runnable chapter demos
└── diagrams/ # White-background Archify diagrams
Next: Context Engineering
In the next post, we’ll dive into Context Engineering — the foundation that makes everything else work. You’ll see how a simple HTTP server prompt produces dramatically different output when constrained by an ADR requiring structured JSON logging.
All code examples are in Go 1.23+. Diagrams use white-background Archify for presentation-ready architecture visualizations.