Enterprise AI Agent Governance & Operationalization Platform
AESOP Transformation OS
A governance-first operating system that takes an enterprise from “we should use AI” to governed, evaluated, deployed, and monitored agents running at portfolio scale. Born from the merger of multi-agent evaluation and certification with a full discovery, design, and build pipeline.
Deterministic and probabilistic methods working in concert. Human-in-the-loop early and often, at every gate. A hard safety/bias veto. Three adoption-planner paths. The Eleven Tenets embedded as code at every gate.
01
Pipeline stages12
Projects
Top-level grouping — all pipeline artifacts (discovery, design, build, evaluation, repair) under one project. Pipeline progress indicators per stage.
Working Agreement
Signed scope and RACI document. Design is hard-gated behind a signed agreement, with a manual override for internal projects.
Discover
Structured interview — stakeholder map, autonomy classification, commitment readiness. A GO / NO-GO / CONDITIONAL triage verdict.
Design
Full PRD generation and architecture scoring across platforms. An 11-dimension gap analysis.
Build
A 6-phase pipeline: Fitness Check, Gap Analysis, Workflow Design, Write Instructions, Generate Artifacts, Summary. Nine quick-start patterns.
Deploy
Ship certified agents to target platforms. Build-session selector, real-time deploy status, dry-run support, launch-package integration.
Operationalize
Four launch artifacts in one API call: RACI Matrix, Tiger Team Brief, Maintenance Plan, Adoption Plan. All from existing pipeline data.
Evaluate
Seven modes: Trace Analysis, Standard Analysis, KB Audit, Custom Rubric, Bias Probe, Investigation, Adversarial Testing. Weighted scoring with a hard safety/bias veto.
Repair
Extended-thinking instruction rewrite from evaluation failures, Tiger Team findings, and trace taxonomy. Closes the loop between evaluation and improvement.
Monitor
Production-agent surveillance — ingest interaction traces, detect drift (a 0–100 score with per-interaction adherence), safety issues, and edge cases. SSE-streamed sweeps with alert generation.
Transcript Review
Meeting-transcript ingestion + LLM extraction of nine portfolio change types (status, milestones, risks, budget, timeline, scope, decisions, actions). Every change routes through HITL review with source-quote traceability before application.
Track
Score trajectories, version diffs, cost tracking, a portfolio health dashboard, drift alerts, maintenance schedule, north-star metric trends.
02
Evaluation modes7 complementary approaches
Trace Analysis
Qualitative failure coding: open coding (human) → axial coding (agent) → taxonomy review (human) → Repair.
Standard Analysis
A 5-agent pipeline — Instructions, Ethos, Bias, Safety, Synthesis. Certification from Bronze to Platinum.
KB Audit
Knowledge-base completeness and coherence checks for retrieval agents. Per-document issues and suggestions.
Custom Rubric
Domain-specific weighted criteria generated from the agent’s own instructions.
Bias Probe
Differential fairness testing across demographic variations with controlled scenarios.
Investigation
Black-box endpoint discovery for evaluating external agents with unknown instructions.
Adversarial Testing
Red-team testing with attack personas probing boundary violations.
03
AI Adoption Planner — three paths
Enterprise — authenticated
Adoption Roadmap Full org-context synthesis. Departments, readiness assessment, strategic prioritization, governance gatekeeper rules, edit mode, export (Markdown + PDF), version history, source manifest.
SMB — public, no auth
SMB Intake A 6-step wizard. Confidence-scored form. Background roadmap generation. Token-based access. Admin review gate before the customer sees results. Trace viewer.
Executive — public, no auth
Executive Intake A 29-field intake. A 3-pass pipeline: Traits Analysis → Roadmap Synthesis → Executive Roadmap. A 5×5 AI Maturity Model. Full provenance graph.
04
Organization & portfolio layer
Multi-org
Users belong to multiple organizations; an org switcher; all data isolated by organization_id with RLS.
Departments + Team
CRUD plus CSV import for departments and team members. Team members don’t need platform accounts.
Portfolio view
A sortable table with inline scoring and a color-coded Kanban board (Backlog, On Deck, Discovery, In Progress, Done) with WIP limits.
Prioritization agent
Synthesizes org context + departments into scored AI project candidates. Seeds the portfolio and adoption roadmaps.
North-star metrics
Measurable outcomes defined per project, tracked through every stage (Build Phase 5 anchoring, Discover alignment, Evaluate NS_ALIGNMENT).
Org context
13 enterprise / 6 SMB context types. File upload with auto-extraction. A dysfunctionality profile for maturity mapping.
Deep research
Enterprise-only 5-phase pipeline: Scope → Search (Tavily + SEC EDGAR) → Fetch → Verify (two-vote adversarial) → Synthesize. A human review-before-apply gate.
Output documents
All pipeline artifacts aggregated per project. Color-coded doc-type tags, download, versioning, delete. KB Audit reports included.
05
The Eleven Tenets — embedded at every gate
1State Change
Every agent articulates the specific state change it creates.
2Problems Before Solutions
Explore the problem space first; reject if better solved by a process change.
3Evidence Over Eloquence
Cite sources; distinguish authority from inference.
4Know What You Need
Classify outputs as deterministic, non-deterministic, or the dangerous middle.
5People Are the Center
Map human experience; assess translation debt.
6Humans Decide
Agents propose, humans choose; a veto needs no justification.
7Multiple Perspectives
Completeness over consensus.
8Context and Brevity
Enough to evaluate, not enough to overwhelm.
9Guardrails Not Gates
Five questions: evaluation, testing, adoption, maintenance, sustainability.
10Trace the Connections
Map second- and third-order effects.
11Questions Stay the Same
Standardized inquiry enables portfolio-level learning.