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ADLC is the right methodology.
Methodologies don't run at 3am.

Glean's Enterprise Agent Development Lifecycle is a real answer for the CIO mental-model question "how do we govern AI agents." Operations Partner is the answer to "who actually runs the seven stages on Monday morning." Same buyer-frame, different commitment — and we will tell you honestly when Glean is the right answer.

Book a 30-min discovery call → Operations Partner overview

Glean is a $7.2B platform with a 250-person sales team and a methodology CIOs already understand. We integrate with Glean as a knowledge source. This page is honest about when Glean fits, when JieGou fits, and when both fit together.

§1 — ADLC mapped to 10-layer governance + delivery

Seven ADLC stages. Ten governance layers. One delivery commitment.

Each ADLC stage maps to one or more JieGou governance layers — and to a specific deliverable from the Operations Partner side. Read this as "who does what" not "who is better." Glean's framing is correct; the operating model is the structural difference.

ADLC stage 10-layer governance mapping Operations Partner delivers
1. Opportunity
Identify a candidate AI use case worth investing in
 Layer 1 — Use-case fit + ROI baseline Discovery call surfaces the workflow; honest "this is not the right shape for an Operations Partner" answer if the math says so
2. Design
Specify agent behavior, tools, and guardrails
 Layer 2–3 — Recipe + workflow design with approval gates and tool scopes Recipe / workflow co-authored with the operator; JieGou ships the first version
3. Performance
Define what good looks like; build evaluation rubrics
 Layer 4 — Bakeoff harness with LLM-as-judge + ground-truth eval sets JieGou runs the bakeoffs; operator reviews outputs; rubric scoring is part of the deliverable
4. Input
Connect the agent to enterprise knowledge sources
 Layer 5 — Knowledge integrations + sensitivity labels + retrieval auditability JieGou connects 13 knowledge sources (Coveo, Glean, Elasticsearch, Algolia, Confluence, Notion, Drive, SharePoint, etc.) and maintains the connection
5. Develop
Build and iterate the agent
 Layer 6 — Multi-LLM provider-portable runtime (Anthropic / OpenAI / Google) with BYOK + circuit breakers Engineering happens inside JieGou; operator does not need to hire a platform engineer to maintain it
6. Launch
Roll the agent into production
 Layer 7 — Shadow Mode → Tier 1 → Tier 2 → Tier 3 trust escalation JieGou runs the trust-escalation cadence; named human operational owner on the JieGou side
7. Monitor & Improve
Track performance; iterate; retire when surpassed
 Layer 8–10 — Hash-chain audit trail + drift detection + retirement policy JieGou owns the on-call rotation, the QBR cadence, and the retirement decision; CIO consumes outcomes

§2 — Buyer fit, honestly

Three audiences. Three answers. We are not always one of them.

Fortune 500 with internal AI engineering teams
Pick: Glean (ADLC + platform)
You have the engineering depth to operate the ADLC stages in-house. Glean is the right tool: the methodology + platform are best-in-class. You staff the lifecycle; Glean is the system of record.
If you already employ 6+ ML / platform engineers and a Chief AI Officer with a real charter, Glean fits your operating model.
Engineering-led mid-market ($50M–$500M) without an in-house AI ops team
Pick: Operations Partner (10-layer governance + delivery)
Your CIO sees the same buyer-mental-model the ADLC describes — but you don't have the headcount to operate it. JieGou is the team that runs the ADLC stages for you. The deliverable is the agent running cleanly at 3am, not a methodology doc.
If your IT team is 8–30 people and AI is one priority among many, the FTE math does not support hiring a platform engineer for a 1–4-pipeline AI roadmap. Operations Partner is the structural answer.
Early-stage AI experimentation (no production workflows yet)
Pick: Neither — yet
Both ADLC and 10-layer governance are production disciplines. If your workflows are prototype-stage, the discipline is overkill. Build the prototype first; bring in the operating discipline when you have a workflow you intend to run in production.
We will tell you honestly in discovery if this is your situation. Operations Partner shape starts paying off at workflow #1 going into production, not at the prototyping bench.

§3 — Side-by-side, 10 dimensions

What you actually buy, who runs it, who is on-call at 3am.

Dimension Glean (ADLC + platform) JieGou (10-layer governance + Operations Partner delivery)
What you buy A methodology (ADLC) + a platform (Glean Work AI + Glean Agents) An Operations Partner who runs the 10-layer governance + 13 knowledge sources + multi-LLM runtime for you
Who runs the lifecycle stages Your internal AI engineering team JieGou — named human operational owner on our side
Who is on-call at 3am Your platform engineer + Glean support queue JieGou's named owner; CIO has one number to call
Headcount required from you 1–2 platform engineers + 1 AI lead minimum to operate the stages CIO + one IT director liaison (no AI-specific FTE)
Time to first production workflow Months — staffing + integration + lifecycle ramp 30-day pilot → first production workflow → quarterly cadence
LLM provider model Primarily Glean's embedded stack (downstream model providers behind the curtain) BYOK across Anthropic + OpenAI + Google; per-provider circuit breaker; you own the keys
Audit trail Platform-internal logging Hash-chain audit trail (regulator-grade); board-defensible artifacts on demand
Deployment model Glean-hosted SaaS (their cloud) Customer-VPC default (Shape B); SaaS available for non-regulated workloads
Pricing model Platform license + per-seat / per-agent tier Engagement fee + annual ops support — flat to your seat count and agent count
Exit Data export + cancel subscription; methodology stays with you Runbook + Shape-B handoff + training; the operation can be brought in-house when in-house is ready

§4 — The eight RFP filter questions, answered

CIO RFP filters from peer-network research. Our answers, in writing.

These are the eight filter questions r/CIO peer-network discussions surface most often when evaluating AI vendors. We answer them on the page so you can compare answers across vendors before discovery.

"How does the vendor define [auto-resolved / deflection / accuracy]?"
We define operational success as "the agent ran the workflow end-to-end without human intervention, the output passed the rubric, and the audit trail shows the decisions." We will share the rubric and the eval set during pilot scoping. We do not quote inflated category numbers; we report what your workflow actually does on your data.
"Show me a reference customer at >X% in our category in our headcount range we can call."
We are pre-named-reference. Our lighthouse customer (engineering-led mid-market manufacturer) is mid-pilot under NDA. We will introduce you when named-reference unlock ships. Until then: we share anonymized architecture case studies and we run a pilot in your environment so the reference is your own data.
"Multi-turn behavior when the user changes scope 4–5 messages in."
Layer 6 (orchestration) handles scope-shift via convergence loops and approval-gate re-entry. We will demo this in your environment with your workflow during pilot scoping.
"Support escalation SLA for a P1 issue, and the cost to us if you miss it."
Named operational owner on our side; P1 acknowledgement under 30 minutes; service-credit terms in the engagement contract. Not best-effort language.
"OAuth scope sprawl on install — least-privilege grant or read-write everything?"
Least-privilege by default per Layer 5 (knowledge-source scopes). We negotiate the exact scopes per integration during pilot scoping; default posture is read-only on the specific labels needed, not read-write across all surfaces.
"Prompt and embedding retention — vector store keeps content for how long after delete?"
BYOK + Shape-B deployment means embeddings live in your VPC, governed by your retention policy. We do not maintain a vendor-side vector store as a separate data layer.
"Downstream model providers — who is actually behind the curtain?"
Anthropic Claude (primary), OpenAI GPT, Google Gemini — declared explicitly per workflow. BYOK means the contract is between you and the provider; JieGou is not a reseller layer between you and the model.
"Operational overhead required after deployment — taxonomy cleanup, prompt tuning, knowledge curation hours/week?"
Zero hours/week from you. That is the deliverable. Our ops team handles taxonomy maintenance, prompt iteration, and knowledge-source curation as part of the annual ops support. The hours-per-week question is what separates an Operations Partner from a vendor.

§5 — Bottom line

ADLC is the methodology. Operations Partner is the team that runs it.

Glean codified what mature enterprise AI operations look like as a methodology. CIOs read the ADLC and know what good looks like. That is real value, and it is why Glean is positioned the way it is.

The structural question for mid-market CIOs is not whether the methodology is correct. It is who runs the seven stages on Monday morning. Fortune 500 buyers staff this with named teams. Mid-market buyers without an in-house AI ops capability either hire a platform engineer (rational at 5+ pipelines) or bring in an Operations Partner (rational at 1–4 pipelines).

Pick the structure that matches the work — not the framework with the largest sales team.

FAQ

Glean-vs-JieGou questions from real CIO conversations.

Book a 30-min discovery call. We'll tell you honestly whether Glean or Operations Partner is the right shape.

No deck. No demo. We walk through your AI roadmap, your in-house ops capacity, and your existing Glean / knowledge-source footprint — and tell you whether Glean, Operations Partner, both together, or neither is the right answer for your situation.

Schedule discovery call → Reference architecture