Move from “we should use AI” to agents that work

Most AI projects fail because they solve the wrong problem. AESOP captures your org structure, strategic priorities, and where work gets stuck before anyone starts building. You get a living map of your organization that every subsequent AI project is grounded against.

• A single dashboard showing every AI project, its stage, and whether it’s on track.
• A department roster mapping who decides what.
• An AI adoption roadmap auto-generated from your own data.

Without this you’re guessing. With it, every AI investment traces to a strategic priority and a named owner. When the board asks what you’re getting from AI, you have the answer.

Every AI initiative has a named sponsor, a measurable goal, and an agreed scope — signed before a dollar is spent on design. A Working Agreement locks in who is responsible, what success looks like, and how quickly stakeholders respond. Design and build are gated: until it’s signed, no work proceeds.

• A signed document with a north-star metric — one measurable outcome.
• A named executive sponsor.
• Expiration tracking so commitments stay current.

AI projects without executive sponsorship die quietly. This puts someone with authority on the hook before resources are committed. The north-star metric becomes the yardstick for every stage that follows.

Before any design or build work, a structured assessment examines the problem, the stakeholders, and whether the organization can absorb the change. You get a clear verdict: GO, NO-GO, or CONDITIONAL. The platform is designed to say no when conditions aren’t right.

• A documented Opportunity Brief.
• A GO/NO-GO decision with reasoning visible.
• A stakeholder map — who’s affected, who sponsors, who owns the outcome.

The most expensive AI mistake is building something nobody needs or uses. This gate catches it before it consumes an engineering hour. Conditional verdicts say exactly what must change before you can proceed.

AESOP generates a full PRD from the discovery work, then scores each platform option — Glean, Claude, OpenAI, or a custom build — across feasibility, capability, and fit. An 11-dimension gap analysis flags anything that can’t be built as specified before work begins.

• A full PRD you can share with stakeholders.
• A side-by-side platform comparison with rationale.
• Red flags for unmet requirements — raised before money is spent, not after.

Platform lock-in is expensive. Choosing the wrong tool because “we already pay for it” yields agents that underperform or can’t do the job. This gives you an evidence-based recommendation you can defend.

The build process writes the agent’s system instructions in the open. Before the agent is finalized, a human must approve the gap analysis — a hard stop, not a dismissible notification. You see what the agent will do, what could go wrong, and what ripple effects to watch.

• Visible, editable agent instructions — not a compiled black box.
• A required human approval checkpoint.
• A summary of what will change, what could break, who needs to know.

Shadow AI is a governance nightmare. This makes every build transparent. When regulators ask how an agent was developed, you have a documented trail with human decision points.

Seven evaluation methods test every agent before deployment. Adversarial red-team probes boundary violations; bias testing checks for unfairness across demographics. A hard veto caps the total score when safety or bias falls below 75% — no exceptions, no overrides. You can prove every agent was tested before it went live.

• A certification badge — Bronze through Platinum — for every agent.
• A pass/fail verdict with the veto rule visible.
• Downloadable evaluation reports, findings linked to source evidence.

One AI safety incident can cost millions in reputation and penalties. This is documented proof of due diligence. When the board asks how you know it’s safe, you have the receipt.

One click produces the four documents an organization needs to adopt, maintain, and get value from every agent — all generated from data the platform already collected during discovery and design.

• A RACI matrix — who owns what.
• A Tiger Team brief — what changes for stakeholders.
• A maintenance plan — when it’s re-evaluated, when it’s decommissioned.
• An adoption plan — phased rollout with readiness gates.

The #1 cause of AI failure isn’t technical, it’s that nobody uses the thing. This stage ships every agent with an adoption strategy, not just a deploy button. Maintenance plans stop orphaned agents running unattended.

A portfolio dashboard shows all agents with current scores, cost tracking, and drift alerts. When performance drops or an agent hasn’t been evaluated in 30 days, you get an alert. Every agent carries its named sponsor and product owner from the Working Agreement — they own it for life. No single-architect bottleneck.

• Score-trajectory charts for every agent.
• Version history with diffs.
• Drift and staleness alerts.
• Per-agent and aggregate cost tracking.
• North-star metric time series.

AI agents drift and requirements change. Without continuous monitoring you only learn an agent has degraded when someone complains — or when it causes harm.