Breadcrumbs
A guided builder concierge. From idea to build — one breadcrumb at a time.
The Problem
Inside most enterprises, there's a quiet bottleneck killing internal innovation: non-technical employees have ideas but no idea how to build them. The internal builder ecosystem is dense — 10–15 approved tools across no-code platforms, low-code app builders, AI agent platforms, IDE-based code-first tools, workflow automation, and BI/dashboarding. Each tool has its own access path, governance rules, skill floor, and data classification limits.
The result: employees give up on ideas, build in the wrong tool and hit a wall mid-project, burn weeks chasing unnecessary access, or build something non-compliant. Generic AI chatbots make it worse — they recommend tools outside the approved catalog, invent access paths, and skip governance entirely.
The Solution
A guided builder concierge that walks any employee — non-technical or experienced — step by step from "I have an idea" to "I have a working build," using only enterprise-approved tools, with governance baked in at every step. Named Breadcrumbs after the Hansel and Gretel fairy tale — because the agent's job is to leave a clear trail so users never get lost in the builder forest.
Unlike generic chatbots, Breadcrumbs answers four harder questions:
- What's the right-sized build for my idea?
- Which approved tool fits — and which don't?
- What's going to block me, and how do I get unblocked?
- How do I move forward safely (governance, data classification, permissions)?
Architecture
Key Architectural Decisions
| Decision | Why |
|---|---|
| Declarative agent on enterprise Copilot Chat | Native access for every employee, zero deployment friction, SSO inherited, fast iteration |
| Single-doc knowledge source | One file = single source of truth = easy to update without retraining anything |
| Stateless by design | Every session starts fresh — no Graph context, no people lookup, no file searching. Trust + governance over personalization |
| Deterministic right-sizer + LLM artifact gen | Tool recommendation is rules-based for accuracy; artifact generation uses LLM for natural language. Best of both worlds |
| No invented tools | Hard-coded approved catalog. Agent must redirect users away from non-approved or deprecated tools |
| User-driven artifact delivery | One artifact per response, user picks the order. Solves UX and performance simultaneously |
What I Built
- A 4-section knowledge source (~6,000 words) covering the full enterprise builder ecosystem — approved tools, governance rules, deprecated tools to redirect from, and 4 artifact templates
- An 8-question structured intake that doubles as a governance check (data classification is the gate question — required before any recommendation)
- A hardened instructions block (~7,200 characters) enforcing pacing, grounding, length, tone, and edge cases — solved real drift issues like agent inventing tools and pulling unintended personal context
- A brand: name, tagline, visual identity (storybook illustration icon), and a fairy-tale-anchored narrative that gave the agent a personality leadership wanted to share
- A demo-ready agent that walks users from idea → recommendation → artifacts in roughly 30 minutes per session
Lessons Learned
- Grounding > intelligence. The agent doesn't need to be smart — it needs to refuse to be wrong. Strict source rules + an approved catalog beat any fancy reasoning.
- Instructions are the product. The knowledge source matters, but the instructions block is where the personality, pacing, and trust live.
- Stateless is a feature. Once I framed Breadcrumbs as a concierge (fresh every session) instead of a memory-based assistant, every governance question got easier.
- Brand is part of the build. Naming it Breadcrumbs (not "BuilderBot") gave the agent a story. That story is what made the demo land.
- Constraints make better products. A 10-hour deadline, an 8,000-character instructions cap, and a 12-tool catalog forced sharper decisions than a 6-week timeline would have.
Command Stack
Enterprise Copilot Chat (Declarative Agent) | Hardened Instructions Block
Single-Doc Knowledge Source | Decision Tree Logic
LLM Artifact Generation | Template-Locked Outputs
Current Status
v0.1 shipped to internal review, rolling out to first enablement team. Video demo coming soon. v0.2 planned: tighter artifact templates, automated ticket creation, and companion agents in the fairy-tale family.