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Our Philosophy

AI Agents Don't Need Human Org Charts

The industry copies human roles onto AI—Dev agent, BA agent, QA agent. But AI agents don't have human limitations. The real problem is governance.

Why Not Dev / BA / DA / QA?

Traditional roles exist because humans have cognitive limitations. AI agents don't share these constraints. Creating 10 specialized agents is solving a problem that doesn't exist—and creating orchestration complexity that does.

Human LimitationTraditional SolutionAI Needs?Why Not
Narrow cognitive bandwidthSpecialize: Dev only codes, QA only testsNoLoads any skill set on demand
Years to learn a domainCareer paths, seniority, titlesNoLoads context in milliseconds
Context-switch is costlyEach person stays in one domainNoSwitches frontend/backend/DB instantly
Self-review biasSeparate QA team reviews workYesLLMs also have bias when self-reviewing
High communication overheadMeetings, Jira, handoff docsNoContext passed via structured data

Only one row says “Yes”—self-review bias. That's the problem SidStack solves.

Governance-First, Not Orchestration

Traditional Multi-Agent

BA Agent
Dev Agent
QA Agent
Fixed roleFixed skillFixed promptper agent × 3
Same LLM inside every agent
N×N orchestration complexity
No learning between sessions

SidStack Governance

WorkerReviewer
Dynamic skills:implement/featureimplement/bugfixdesign/api+ specialty tag
Dynamic skills:review/codereview/securityreview/performanceIndependent verify
1 handoff, not N×N
Skills are pluggable files
Learning loop built-in

A Worker with the frontend specialty loading implement/feature.md is your “Frontend Dev.” The same Worker loading design/api.md becomes your “API Architect.” No new agent needed—just a different skill file.

The Learning Loop

Most AI agent frameworks start every session from zero. SidStack captures mistakes, extracts lessons, creates reusable skills, and enforces rules—so the same error never happens twice.

1

Incident

Agent makes a mistake or encounters confusion

2

Lesson

Root cause analyzed, prevention documented

3

Skill

Reusable procedure created from lessons

4

Rule

Mandatory check enforced for future sessions

Each step is role-aware. A Worker's frontend mistake becomes a lesson that improves future frontend tasks.

Quality Gate by Design

The #1 Risk: Self-Review Bias

When an LLM reviews its own code, it tends to confirm its own reasoning—just like humans. Hallucinations pass self-review. Security flaws go unnoticed. This is the fundamental risk of AI-generated code, and no amount of specialized roles fixes it if the same agent implements and reviews.

SidStack's Solution: Enforced Separation

Worker implements. Reviewer verifies. They are architecturally separate—not the same agent wearing different hats. The Reviewer gets independent skills with structured checklists:

Code Review

Correctness, quality, dead code, naming

Security Review

OWASP Top 10, injection, auth, secrets

Performance Review

N+1 queries, data structures, complexity

Side-by-Side Comparison

AspectMulti-Agent (Dev/BA/QA)SidStack
Active roles5-10 fixed identities2 governance roles
Adding capabilityCreate a new agentAdd a skill file
OrchestrationComplex N-to-N routingSingle Worker → Reviewer handoff
Quality assurancePer-agent, inconsistentUnified quality gates
Learning from mistakesNone (starts fresh)Incident → Lesson → Skill → Rule
FlexibilityLocked to role identityDynamic skill loading per task

Ready for governance-first AI?

Free and open source. 2 roles, dynamic skills, quality gates, and a learning loop that makes every session smarter than the last.