Why Your Agentic Workflows Don't Work

Why Your Agentic Workflows Don't Work

This video, presented by an AI builder and consultant, explains why agentic workflows and AI-powered development projects often underperform despite impressive demos online. The creator identifies three core failure modes that undermine even well-intentioned AI builds, particularly for advanced beginners and intermediate users, and offers concrete solutions for each.

🧠 1. Weak Planning Cycle

The most critical failure is a shallow planning process. Successful builders spend substantial time in a detailed, multi-phase planning loop before any building happens:

• Exploration Phase: Research the subject domain, understand relevant technologies (SDKs, APIs), identify best practices, and map where the new feature fits into your existing codebase.
• Proposal: Document the "why"—the motivational intent that guides model decisions when ambiguity arises.
• Design: Define architecture, data models, API endpoints, and system-level decisions.
• Specs & Tasks: Create feature specs with acceptance criteria, then break work into actionable tasks.

"You have to have this type of very detailed planning process or else you are just building on the worst foundation possible."

✂️ 2. Context Rot / Poor Pruning

Agents generate massive amounts of context and ideas—most of which are distracting. Without aggressive pruning, models overengineer, balloon scope, and produce incoherent results that stray from your intent.

• Ask: Is this context valuable? Should it be integrated now?
• Push non-essential ideas to a backlog (e.g., GitHub issues, Linear) for later consideration.
• Use skills like "grill me" or pony-tail-style auditing to catch overengineering before it contaminates the build.

Result: A lean, token-efficient planning phase and a focused build phase.

🏗️ 3. Missing Opinionated Build System

Relying on skills alone is a recipe for disaster. You need a structured, spec-driven development lifecycle with guardrails and defined phases. Two recommended tools:

• OpenSpec: Explore → Propose → Generate artifacts (proposal, design, specs) → Apply → Sync back to master specs. Flexible for mid-flight updates.
• GitHub Spec Kit: More rigorous, phase-gated workflow. Features a Constitution file (validated against every feature), requirements, user stories, API contracts, and task planning with dependency mapping.

"If you do not have a structured system for building the things that you want to build... you have a horrible time."

Key Takeaway

Robust agentic building requires a rigorous planning loop, aggressive context pruning, and structured SDLC tooling to enforce guardrails and maintain coherence.