Salesforce delivery work is full of high-value moments that require structure, context, and judgment.

Before a solution reaches production, teams need to prepare discovery sessions, translate notes into requirements, estimate effort, plan QA, support UAT, triage defects, assess go-live readiness, and create technical documentation.

Each of these activities matters. They help teams reduce ambiguity, align stakeholders, validate the solution, manage risk, and make better project decisions.

But many of these activities also involve repeatable documentation, analysis, and formatting work.

Project teams often start from similar structures again and again: discovery agendas, requirements pages, QA plans, UAT test cases, defect summaries, release readiness reports, and handoff documentation. The work still requires human judgment, but the starting point is often manual, time-consuming, and inconsistent across projects.

That is the opportunity I wanted to explore:

How can AI agents help Salesforce delivery teams create stronger first drafts, improve traceability, surface gaps earlier, and spend more time on the work that requires real consulting judgment?

Why I Built This in Glean

I used Glean to build a suite of AI agents designed around the Salesforce delivery lifecycle.

The goal was not to create generic prompts. The goal was to design practical agents that support real delivery moments and work with the kinds of artifacts project teams already use: SOWs, kickoff notes, discovery notes, transcripts, Confluence pages, Jira defects, QA plans, UAT plans, release notes, and technical documentation.

That distinction matters.

An isolated prompt can help with a one-off task. But a well-designed agent can support a repeatable workflow.

For this suite, each agent is focused on a specific delivery need: preparing for discovery, generating requirements, estimating ROM, planning QA, supporting UAT, triaging defects, evaluating go-live readiness, or creating documentation.

The platform is important, but the bigger lesson is the workflow design. AI agents become more useful when they are grounded in the artifacts, tools, and decisions that teams already rely on every day.

The Delivery Problem

Salesforce projects are complex because every project brings its own mix of scope, stakeholders, business processes, integrations, data considerations, risks, and constraints.

Even when the delivery methodology is consistent, the project details are always different.

That creates a challenge: teams need enough structure to stay consistent, but enough flexibility to adapt to each project.

Without that structure, common issues can appear:

• Discovery questions may be too generic.
• Requirements may not be detailed enough for implementation.
• QA plans may not fully trace back to requirements.
• UAT test cases may be too technical for business users.
• Defects may be counted but not interpreted.
• Go-live decisions may rely on scattered updates.
• Technical documentation may start from fragmented notes and screenshots.

None of these are new problems. They are normal delivery challenges.

But they are exactly the kinds of problems where AI agents can help create stronger structure around the work.

The Agent Suite

The suite I built follows the Salesforce delivery lifecycle.

The core agents support:

1. Discovery preparation
2. Requirements generation
3. ROM estimation
4. QA planning
5. UAT planning
6. Defect triage
7. Release and go-live readiness
8. Technical documentation

Each agent has a focused role.

The Discovery Prep Generator helps consultants prepare for better discovery sessions.

The Requirements Generator turns discovery outputs into structured, implementation-ready requirements.

The ROM Generator reviews documented requirements and compares estimated effort against the SOW.

The QA Plan Generator creates internal QA coverage from functional requirements and acceptance scenarios.

The UAT Regression Planner creates business-user-friendly UAT test cases.

The Defect Triage & QA Insights Agent helps teams understand defect patterns, root causes, retest priorities, and release impact.

The Release & Go-Live Readiness Agent evaluates whether a project is ready for production and recommends Go, Go with Conditions, or No-Go.

The Technical Documentation Builder turns raw project notes, screenshots, and implementation details into polished documentation.

The point is not that every project needs every agent every time.

The point is that each agent supports a specific delivery moment, and together they create a more connected workflow from discovery to go-live.

Why the Workflow Matters

The biggest opportunity is not just using AI to generate individual documents faster.

The bigger opportunity is creating a connected delivery workflow.

Discovery informs requirements. Requirements inform estimation. Requirements also inform QA and UAT. QA and UAT produce defects and feedback. Defects influence release readiness. Release readiness informs the go-live decision. Technical documentation captures what was implemented and how it should be understood.

When those artifacts are disconnected, delivery risk increases.

A missed detail in discovery can become an unclear requirement. An unclear requirement can become a test coverage gap. A test coverage gap can become a late defect. A late defect can become a go-live risk.

A connected agent workflow can help strengthen those handoffs.

Agents Support Judgment — They Do Not Replace It

I do not see these agents as replacements for delivery expertise.

They do not replace the Solution Consultant’s understanding of the client’s business.

They do not replace the QA Engineer’s ability to think critically about risk.

They do not replace the Project Manager’s responsibility to manage scope, budget, timeline, and stakeholders.

They do not replace the Architect’s or Developer’s technical judgment.

Instead, they help the team start from a better baseline.

They reduce blank-page work. They standardize repeatable structures. They make gaps easier to see. They improve traceability. They give teams more time to focus on the work that requires context, experience, and judgment.

What This Series Will Cover

This post introduces the overall idea behind the Salesforce delivery agent suite.

In the rest of the series, I’ll go deeper into the specific delivery moments these agents support, including:

• Preparing for better Salesforce discovery sessions
• Turning discovery notes into implementation-ready requirements
• Improving traceability across QA, UAT, and defects
• Supporting go-live readiness with clearer decision-making
• Using AI agents as delivery enablement tools without replacing human judgment

As the series develops, I’ll also share the thinking behind the agent design: what inputs each agent needs, what outputs it produces, where it fits in the lifecycle, and how it helps the team make better decisions.

Closing Thought

Salesforce delivery is complex because every project is different.

But the structure of delivery has patterns.

That is where AI agents can help.

When designed around real project artifacts and real delivery decisions, agents can do more than save time. They can help teams work with more consistency, better traceability, and stronger confidence from discovery to go-live.