01 The Workflow

Launch readiness, coordinated by agents

A product launch is one of the most scattered workflows in GTM. Readiness lives across eight tools, status goes stale before it's reviewed, and decisions wait for the next meeting. This walkthrough shows how agentic AI runs that daily coordination — so the launch owner is left with only the decisions.

01

The pain today: information spread across Drive, Slack, Jira, Zoom, email. Readiness hard to assess. Risks surface late. Every status needs a meeting.

02

The move: agents read the tools the team already uses, run the launch cadence, and turn scattered signals into decision-ready briefs.

03

The rule: agents propose, a human decides. Nothing material ships without approval — human-in-the-loop at every real call.

A clean-room illustration of a pattern I designed and deployed for a real GTM program — no proprietary content, just the mechanics.

02 One Screen

Feature X Launch

The single screen the launch owner opens to know exactly where things stand. Today is July 3; go-live is September 15. Readiness signals read live now. Performance signals stay locked until there's traffic — every breach below produces a decision.

Initiative

Feature X Launch

Segment

Mid-Market

Launch Date

September 15

Health

● Yellow

Open Decisions

2

◄ PRE-LAUNCH · readiness

POST-LAUNCH · performance ►

NOW · Jul 3

GO-LIVE · Sep 15

Launch Readiness

78%

On Track · Watch

pre-launch

Audience Quality

72%

Acceptable

pre-launch

Field / Rep Readiness

43%

Below Threshold

pre-launch

🔒 LOCKED

Conversion Health

—

Activates at go-live

post-launch · Sep 15

Decisions Requiring Action

Each breach above → a prepared brief here. Agents propose; you decide.

FIELD · 43%PRE-LAUNCH

Are we ready to launch?

Problem: West-region training incomplete — 43% certified.

Recommendation: Launch East only.

Owner A · East go-live Sep 5 · West cert Sep 7

Approve

Reject

Human-in-the-loop

AUDIENCE · 72%PRE-LAUNCH

Targeting the right audience?

Problem: 340 accounts double-targeted — 22% overlap.

Recommendation: Suppress overlapping accounts.

Owner E · Apply suppression list · Today

Approve

Reject

Human-in-the-loop

CONVERSION · 🔒POST-LAUNCH

Performance decisions activate at go-live.
No funnel exists until there's traffic — so no decision to make yet.

Approve and the metric above re-scores in real time — the loop closes without a meeting.

03 The Engine Behind It

The Signal Engine

Source systems are never replaced. The engine reads them, turns raw facts into ~20 typed signals, routes each to the one agent that owns it, and funnels every verdict through a single human gate. One data shape, all the way through.

SOURCES

The tools the team already uses

DriveSlackJiraZoomEmailConfluenceTeams

↓ read & normalize

SIGNAL LAYER

Raw facts → ~20 typed signals

Every source fact is normalized into one shape — a typed signal. This is the move that lets specialized agents work off the same truth without contradicting each other.

↓ route to the owning agent

INTELLIGENCE

6 agents — each reads only its signals, emits one verdict

Narrow by design: an agent that does one thing is testable and won't drift. The Customer agent is conditional — it wakes only when Funnel raises a drop_flag.

1Launch Readiness

launch_readiness · milestones · blockers · days_to_launch

→readiness_verdict

2Audience

audience_overlap · quality · accts_double

→audience_verdict + suppress_rec

3Enablement

field_cert · training · reps_certified

→cert_verdict + gap_list

4Funnel

win_rate · trials · opp_to_close · pipeline

→funnel_verdict + drop_flag

5CustomerCONDITIONAL

pricing_obj · churn · sentiment · feature_gaps

→root_cause + evidence

6Exec Intelligence

all five verdicts above

→decision_brief

↓ synthesize

EXEC INTELLIGENCE

Verdicts → one decision brief

Problem · recommendation · owners · due dates — the operator gets one brief, not five alerts.

↓ govern

GOVERNANCE

The control plane — human approves

Threshold breach fires an escalation → human approve / reject → audit record. Nothing material happens without a human.

04 Inside an Agent

You don't write code. You write what you want.

Here's the thing people miss: you don't write code. You write what you want, in plain English — the way you'd ask a sharp colleague. This is the Enablement agent, exactly as a marketer would type it.

I'm launching a product and I need to know if my sales reps are ready to sell it.

Check our certification numbers — what % of reps are certified, broken down by region.

If we're at 60% or higher, tell me we're good. If we're below 60%, flag it — but tell me which region is behind, and suggest the least disruptive option, like launching only where reps are ready.

Don't do anything on your own — just give me your read and a recommendation, and I'll make the call.

That's the whole agent. Four plain sentences. Notice what's baked in: one job (sales readiness), the number it checks, the bar (60%), and the rule — it recommends, you decide.

One job

The agent watches a single thing — sales readiness. Nothing else.

What to check

You name the numbers it should look at. It reads, it doesn't touch the tools.

The bar

You set the line — 60%. Change your mind, change one sentence.

You decide

It recommends. You approve. That rule is what makes it safe.

05 The Agent, Live

Now watch it actually run.

Everything before this was the pattern. This is the Enablement agent running for real — it reads the certification data below, applies the 60% bar, and prepares a decision. Nothing is hardcoded: the verdict is computed live. You make the call.

Source · rep_certification.csv · Feature X Launch

Rep	Region	Certified

Change any row above and the verdict changes — because the agent computes it, it isn't a screenshot. In a real engagement, this same agent reads live Jira or your LMS instead of a sample file.

06 The Loop Closes

The same picture, moments later.

This is the operating picture right after the approval — no meeting, no status chase. The agent's decision has re-scored Field Readiness, cleared the open field decision, and moved the launch to a confident state. One approval, and the whole picture updates.

Initiative

Feature X Launch

Launch Date

September 15

Health

● Green · East confirmed

Open Decisions

1

Launch Readiness

85%

On Track

pre-launch

Audience Quality

72%

Acceptable

pre-launch

Field / Rep Readiness

86%

✓ East-only · cleared

pre-launch

🔒 LOCKED

Conversion Health

—

Activates at go-live

post-launch · Sep 15

What changed

The field decision moved from flagged to resolved — driven by the agent's recommendation and your approval.

FIELD · 43% → 86%RESOLVED

Are we ready to launch?

Decision: Launch East only — approved.

Result: Field readiness re-scores to the East-only path; West held for the next cert cycle.

✓ audit logged · Jul 3 · approved by launch owner

AUDIENCE · 72%PRE-LAUNCH

Targeting the right audience?

Still open: 340 accounts double-targeted — awaiting your call.

Recommendation: Suppress overlapping accounts.

Owner E · one decision still needs you

CONVERSION · 🔒POST-LAUNCH

Still locked until go-live.
Performance decisions activate once there's traffic.

This is the whole point: one agent read the data, one human approved, and the operating picture updated — without a status meeting, a rebuilt report, or a wait for the next review. The loop closed in place.

07 One Signal, End to End

The engine in motion

The Signal Engine drawn as one example — field certification's journey from a raw Jira fact to a governed decision. Every path, whichever agent fires, ends at the same human gate.

Source

Jira: 43% West reps certified

→

Signal

field_cert = 43%

→

Agent 3

Enablement: 43% < 60% → breach

→

Brief

Exec Intel: launch East only

→

Human

approve / reject

→

Record

audit logged · re-scores 43→85%

Fan-in, not free-for-all

Five agents run in parallel, each on its own signals. All verdicts funnel into one synthesis agent — the operator gets one brief, not five alerts.

One human gate

Every path ends at the same approve / reject checkpoint. Propose → approve → publish, always.

The honest boundary: these agents sense and decide — they read, verdict, and escalate. They don't yet plan the fix, assign owners, and chase tasks to closure. That execution half is buildable, but it's where governed write-back gets hard. Reading is easy; writing back safely is the real engineering.

08 Business Impact

What it changes

For this one launch workflow, the gain isn't headcount — it's the recovery of senior attention. Time moves from status-chasing to judgment. Figures illustrate the pattern's mechanics, not a guaranteed outcome.

Metric	Before		After
Readiness assessment	~5 days	→	~30 minutes
Decision latency	~3 weeks	→	~2 days
Reporting effort	~4 hours	→	~15 minutes
Risk detection	Weekly	→	Continuous

80%

Adoption target

2,000+

Est. annual hours reclaimed

1+

Approx. FTE-equivalent capacity

Figures illustrate the pattern's mechanics, not a guaranteed outcome. Real results depend on program size, tool access, and adoption.