Skip to content
Agent DevOps / Salesforce
Introduction video Audio · Debate (22 min) Problem vs Solution Agent Development Federated Architecture Spec-Driven Development SDD + Agent Framework Practical Example Orchestrating Devin Costs & ACUs Workflow Repository Structure Governance & Checklist Author & Credibility Resources
Salesforce DevOps · Federated Model · Devin + Flosum

How to develop Salesforce with AI Agent without generating technical output

A practical and teaching guide for teams Apex, LWC and Admins want Stop typing bullerplate and start to orchestrate business solutions using Spec-Driven Development, Devin (Cognition) and Flosum as Source of Truth.

This is not a new method — it is the disciplined application of good engineering practices (explicit contracts, clear borders, deploy governance) to the ecosystem Salesforce with Devin and Flosum.

The Problem

AI without context generates inconsistent code, metadata conflicts and Technical output that scale faster than the feature.

In Salesforce it gets worse

Shared orgs, implicit rules, conflicting profiles and legacy makes each deploy a minefield.

The solution here

Spec-Driven Development + autonomous agents + Flosum as SoT, in a teaching framework step by step.

Start with SDD View Federated Architecture Github. with/issei/sf-repo
Template
Federated
Multi-time, multi-repos, no mono-repo
Method
Spec-Driven
Structured templates orchestrate AI
Source of Truth
Flosum CLI
Versioning, merge and promotion
Auth
sf · Web Login
No JWT, no local keys
Format
Practical guide
Depth
Practical guide · ~5 min for central idea · ~19 min complete

You prefer the essentials first? Read synthesis (~1 min)

In 1 video

Video summary explanation

If you prefer to start with the visual panorama before diving into the sections, this video covers the central points of the framework — Agentic, SDD and Flosum pipeline — in direct format.

Video on YouTube · executive summary
Open on YouTube

The context

Why IA + Salesforce, in the traditional way, scale break

There are two overlapping problems: the naive use of generative AI in the code and traps intrinsic Salesforce (sharedorgs, implicit rules, legacy integration). When these two worlds collide without method, the result is exponential technical output.

Development AI problems

  • Code inconsistent — each session produces a different pattern for the same problem.
  • Lack of context — the AI knows no conventions, naming, Ownership or the legacy of the team.
  • Silent Hallucinations — APIs and fields that They look like exist but do not exist in org.
  • Accelerated technical debt — code accepted without critical review It becomes a legacy in weeks.
  • Uncontrolled cost — vague prompts consume tokens/ACUs in "Tentative and error" loops.

Salesforce specific problems

  • Complex and shared orgs — Multiple teams playing QA/Prod at once.
  • Implicit rules — Profiles, Permission Sets, Validation Rules and Flows that nobody documented.
  • Integration with legacy — 10-year-old Apex, giant triggers, hidden dependencies.
  • Metadata trampling — one team overwrites components of another Quietly.
  • Source of Truth ambiguous — Git and Org differ, and no one knows What's the truth?

The turn

Traditional vs. Federated Agent

Multiple teams working on the same orgs of QA, PreProd and Prod — in a single repository — generate conflicts daily, broken deploys and methadata trampling. The Federated Agent Model is the approach structured adopted to deal with this, combining isolation, AI and governance.

Traditional Model

Anti-pattern
  • Monorepo with all the teams stepping on the same metadata.
  • Conflicts constant Profiles, Layouts and Flows.
  • Deploys direct via CLI in shared orgs — risk of overwriting.
  • "Source of Truth" ambiguous between Git and Org.
  • AI without context = generic code, rework and high cost.

Model Agent Federated

Recommended
  • Each team in your own GitHub repo isolated.
  • Ownership by domain + automatic border validation.
  • Devin validates and prepares — Flosum promotes.
  • Specs, Playbooks and skills give rich context to AI.
  • Smart Flosum merge solves team conflicts.

Concept

What is it? Agent Development

Before talking about SDD, you need to understand the paradigm shift. AI as tool is the copylot autocompleting lines; AI as an agent is a collaborator autonomous that reads specs, performs tasks, validates and itera within clear borders. Both worlds require techniques distinct.

AI as a tool

Copilot

You drive every line. The AI fills, suggests, complete. Focus on local speed.

  • ·It acts within the editor, in short scope.
  • ·There's no memory between tasks.
  • ·Dev is responsible for context, validation and architecture.
  • ·Good for: snippets, rename, punctual refactor.

AI as an autonomous agent

Agent

You give direction, context and boundaries. The agent plan, execute, validate and itera alone.

  • ●Read Specs, playbooks, knowledge-base.
  • ●Create branches, encode, run tests, open PRs.
  • ●Keeps state between iterations session.
  • ●Good for: features end to end inside clear borders.

How the agent acts in the development cycle

Agents aren't "IA with more tokens." They are. reasoning loops with access tools (shell, FS, CLI, browser) operating within what you explain as context and limits.

Plan

Read Spec, decompose into tasks, define execution order.

Act

Run: Creates files, CLI wheel, modifies code, calls tests.

Watch

Reads stdout, logs, validation failures and human feedback.

Reflect

Evaluate whether you have fulfilled the criterion of acceptance and replan if necessary.

Origin of the cycle: This loop is inspired by the pattern ReAct (Reasoning + Acting), introduced by Yao et al. in 2022, which describes how language agents combine reasoning and action in successive iterations. Here it is adapted to the Salesforce development context with Devin.
Central Insight: the agent output quality is a direct function of context quality. No context = guesswork. With rich context (Spec + playbook + knowledge-base) = real engineering.

The architecture

Four pillars, well-defined roles

Instead of a monolithic system, we separate responsibilities. Each piece does one thing — and it does very well.

Pillar 1

GitHub

The layer of intention and knowledge.

  • · Specs and Playbooks
  • · Skills and knowledge-base
  • · Pull Requests and Human Review
  • · No, no, no. It's Org's Source of Truth.
Pilar 2 · SoT

Flosum

A Source of Truth and the engine promotion.

  • · Real org state version
  • · Smart Merge between teams
  • · Promotion QA → PreProd → Prod
  • · Only official deploy path
Pillar 3

Salesforce

O rantime and the environments.

  • · Sandbox for validation
  • · QA, PreProd and Prod shared
  • · sf Internet Login
  • · Never deploy direct via Devin
Pillar 4

Devin

O executable/developer Self-employed.

  • · Read Specs and knowledge-base
  • · Encode in force-app/
  • · Validated via --check-only
  • · Prepare package for Flosum

Relationship

How the pillars connect

GitHub captures the intention, Devin executes, Flosum governs promotion and Salesforce is the rantime. Arrows show the real direction of the flow — not desire, which actually happens.

lê Spec + playbooks push branch + PR merge → main (webhook) --check-only sandbox promove QA → PreProd → Prod PILAR 1 GitHub intenção · specs · PRs PILAR 4 Devin executa · codifica · valida PILAR 2 · SOURCE OF TRUTH Flosum smart merge · promoção PILAR 3 · RUNTIME Salesforce QA · PreProd · Prod CAMADA DE INTENÇÃO & EXECUÇÃO PIPELINE GOVERNADO (DEPLOY OFICIAL)

→ continuous

official and canonical path

--- dashed

local validation (does not promote)

Flosum highlighted

single deployer for shared environments

Clusters

separate intention/execution from governed pipeline

Method

Spec-Driven Development — the Heart of the Method

Devin can't guess. He follows clear boundaries. Spec is the contract between the team, the agent and the business -- and the better Spec, the lower the cost (ACUs) and the higher the output quality.

Definition

What is SDD

Spec-Driven Development is a practice in which all implementation is preceded by a specification versioned — a structured document describing context, requirements, technical rules, limits and criteria of acceptance. Spec precedes code and It's the agent's main input.

Spec-Driven Development is not a concept invented here — it relies on practices consolidated as Design by Contract (Bertrand Meyer), BDD/TDD with explicit acceptance criteria and Architecture Decision Records. What this approach does it is to apply these principles to the specific context of autonomous agents in Salesforce.

Source of truth

Role of specification

Spec is the source of truth of intent. Code can change; Spec It captures why. For the agent, she is the briefing; for the team, it is the contract; for the future, it is the documentation that survives the reactor. Versioned at specs/By the code.

Benefits

Why use SDD with agents

  • ●Predictability — frozen scope before code.
  • ●Quality — explicit acceptance criteria.
  • ●Alignment — human and agent read the same contract.
  • ●Cost minor — less rework and spent ACUs.
  • ●Auditability — who decided what, when, why.

Anatomy of a Spec

The 6 sections that every demand must have:

  • 01Background

    Why does this demand exist? What's the deal?

  • 02Functional requirements

    Expected observable behavior in product language.

  • 03Technical rules

    Standards, naming, conventions, integrations and technical limits.

  • 04Out-of-scope limits

    What? no must be played, altered or created.

  • 05Acceptance criteria

    Testable, measurable. "Done" means what, exactly?

  • 06Safety guards

    No direct deploy, no destructivechanges, no JWT, respected ownership.

SDD template (markdown)

specs/<ticket>. md 
# SPEC: [TICKET-123] Validação de desconto em pedidos B2B

## 1. Contexto
Pedidos B2B com desconto acima de 30% precisam de aprovação automática
do gerente regional. Hoje a regra é manual e gera atrasos.

## 2. Requisitos funcionais
- Disparar fluxo de aprovação ao salvar Order com Discount__c > 30.
- Notificar o Owner_Manager__c via email quando aprovação for solicitada.
- Bloquear alterações em Status enquanto aprovação estiver pendente.

## 3. Regras técnicas
- Trigger: OrderTrigger (já existe — apenas estender via handler).
- Padrão: "Trigger Handler Pattern" conforme /playbooks/02_apex.md.
- Cobertura mínima de testes: 85%.

## 4. Limites (out-of-scope)
- NÃO modificar Profiles, Permission Sets ou Layouts.
- NÃO criar novos objetos — apenas Custom Field Discount__c.
- NÃO tocar em metadata fora de CommercePricing.

## 5. Critérios de aceite
- [ ] Order com desconto > 30% inicia approval automaticamente.
- [ ] Email enviado em até 60s.
- [ ] Testes Apex passando com ≥ 85% de cobertura.
- [ ] check-metadata-ownership.py aprovado.

## 6. Guardrails
- Auth: sf org login web apenas.
- Deploy: --check-only em sandbox; promoção via Flosum.
- Sem destructiveChanges. Sem JWT. Sem snapshot completo.

How Devin interprets each section

  • Context → calibrates the tone and scope.
  • Requirements → generates tasks (every plan).
  • Technical rules → chooses right patterns.
  • Limits → avoids files out of scope.
  • Accept → turns validation checklist.
  • Guardrails → It blocks destructive actions.
Anti-pattern: "Create an LWC of requests." → vague, ambiguous, high cost. Always write a Spec.

Framework

The SDD + Agent Framework in 6 steps

A teaching cycle. You can apply tomorrow at any Apex/LWC feature. Each step has clear objective, concrete action and expected result — nothing is optional.

1 Step 01

Context definition

Before Spec, the context.

Objective
Make sure the agent knows where You're acting.
Action
Update knowledge-base/: Ownership, org-inventory, pipeline-map.
Result
Agent can reason about team boundaries and standards.
2 Step 02

Creation of specification

The contract becomes artifact.

Objective
Capture context, requirements, rules, limits, accept and guardrails.
Action
Write specs/<ticket>.md following the SDD template.
Result
Document versioned, revised, teachable and reusable.
3 Step 03

Interaction with the agent (Devin)

Direction, not typing.

Objective
Delegate the implementation tied to Spec and a playbook.
Action
Short prompt: "Read specs/X.md, apply playbooks/02_apex.md, act only on force-app/...".
Result
Branch devin/<ticket>-<slug> with code + tests.
4 Step 04

Validation

Evidence-based trust.

Objective
Prove that delivery meets the criteria of acceptance and guardrails.
Action
Rotate check-metadata-ownership.py, --check-only Sandbox, Apex tests.
Result
Green validations or clear failure for the next iteration.
5 Step 05

Iteration

Spec evolves before code.

Objective
When something fails, refine Spec before asking for more code.
Action
Update Spec → short re-prompt → revalidate. Never itere in the dark.
Result
Convergence in few iterations, no burning of ACUs.
6 Step 06

Deploy via Flosum

The agent never promotes.

Objective
Promotion governed QA → PreProd → Prod.
Action
PR approved → merge in main → Flosum runs promotion via webhook.
Result
Deploy auditable, with Smart Merge solving overlaps between teams.

The loop

Steps 1→6 are not linear: 4 and 5 form a short validation loop

The linear path is Context → Spec → Devin → Flosum. But validation and iteration form a closed loop: Every failure feeds Spec refinement before requesting more code from the agent.

01 Contexto 02 Spec 03 Devin 04 Validar 05 Iterar loop curto refina Spec 06 Flosum → Prod aceite ✓
Linear (preparation) Implementation of agent Loop validation/iteration Promotion governed

Practical implementation

Concrete example: creating an Apex + LWC feature

Royal scenario. Same agent. Same feature. Two approaches. One of them explodes in technical debt in three sprints; the other predictable delivery from day one.

Scene

Automatic approval of orders B2B discounted > 30%

B2B orders with discount above 30% need to trigger a stream of approval for the regional manager. There is an LWC in Order detail that needs to display the status in real time and block editing while pending.

NO SDD Approach

Anti-pattern

Typical prompt

The prompt below is intentionally exaggerated to make the didactic contrast — but less obvious variations of the same pattern (empty scope without explicit limits) they produce the same problems on a smaller scale. 

# Prompt vago e ambíguo
"Faça uma aprovação automática quando o desconto
do pedido for grande. Cria uma tela bonita pra mostrar
o status, e arruma o que precisar pra funcionar."

What happens

  • ●Agent creates a new object Approval__c (There was already a pattern!).
  • ●Stirs Profile and Layout Without Ownership — Break Another team.
  • ●"Big discount" was interpreted as > 50%, not > 30%.
  • ●LWC created with inconsistent naming, no tests Jest.
  • ●Apex coverage at 62%, below the minimum (75%) → Deploy fails.
  • ●3 rework cycles, ~4× plus spent ACUs.
Result: half-ready feature, created technical debit, side team Angry.

SDD Approach

Recommended

Pseudo-Spec (specs/B2B-450. md) 

# SPEC: [B2B-450] Aprovação automática > 30% desconto

## Contexto
Reduzir SLA de aprovação manual em pedidos B2B.

## Requisitos
- Disparar approval quando Order.Discount__c > 30.
- LWC orderApprovalStatus no Order Page Layout.
- Bloqueio de edição em Status enquanto pendente.

## Regras técnicas
- Estender OrderTrigger via Handler Pattern.
- Reutilizar objeto padrão ProcessInstance (NÃO criar Approval__c).
- Cobertura Apex ≥ 85%; Jest ≥ 80%.

## Limites
- NÃO modificar Profiles, Layouts ou outros domínios.
- Apenas force-app/main/default/{classes,lwc,triggers}/.

## Aceite
- [ ] Order com desc > 30% inicia approval em < 60s.
- [ ] LWC mostra status em real-time (Platform Event).
- [ ] check-metadata-ownership.py verde.

Prompt for Devin

Leia specs/B2B-450.md e playbooks/02_apex.md.
Implemente apenas em force-app/main/default/.
Rode check-metadata-ownership.py antes de finalizar.
Pare se qualquer guardrail falhar.

Expected result

  • ●Branch devin/B2B-450-approval-auto with code + tests.
  • ●Reuse of ProcessInstance — no new object.
  • ●Apex coverage 89%, Jest 82%.
  • ●Green validations; PR ready in one session.
  • ●Flosum promotes QA → PreProd → Prod without intervention.
Result: feature delivered, no debit, with full audit.

Lesson 1

All the ambiguity of the prompt becomes rework — paid in ACUs and in Deploy broken.

Lesson 2

Short spec > long prompt. 30 Spec lines are worth more than 300 Prompt lines.

Lesson 3

Explicit limits protect neighboring teams — ownership is not bureaucracy, it's security.

Orchestration

Orchestrating Devin (Web and CLI)

You're not coding anymore. It's giving direction. Devin is the brilliant junior engineer who needs a Tech Lead -- and that Tech Lead is you.

Good practice

  • Use playbooks and refer them to the prompt.
  • Point for Reusable skills ed .agents/skills.
  • Itere ed small steps; validate before moving forward.
  • Rhode --check-only in the sandbox before the PR.
  • Use Agents.md and Blueprints to fix the environment.

Anti-patterns

  • Prompts not used "make an LWC of requests".
  • Scope Giant in a single session.
  • Ask Direct deploy in Prod Devin.
  • Ask destructiveChanges without approval.
  • Save credentials (JWT, refresh tokens) in any file.
Why Web Login (and not JWT): authentication via sf org login web It's deliberately adopted. In Salesforce sandwiches, each dev log in individually, and Individual JWT by developer is operationally unfeasible. Centering on a systemic IC/CD user would eliminate traceability of changes by author — critical audit information and Smart Merge. The Web Login preserves identity without compromising the pipeline.

Effective Prompt (Web/CLI)

Short, directed, anchored in repo files.

# Bom: específico, com referências e limites
Leia specs/TICKET-123.md.
Aplique o playbook playbooks/02_apex.md.
Implemente apenas em force-app/main/default/classes/.
Valide com scripts/salesforce/validate-deploy.sh.
Pare se check-metadata-ownership.py falhar.

Low Prompt

It costs ACU, generates rework, deviates from scope.

# Ruim: ambíguo e sem fronteiras
Faça uma melhoria no fluxo de pedidos.
Tenta deixar mais bonito também.
Se precisar, mexe no Profile pra liberar.
Faz deploy direto na QA pra ver se funciona.

Efficiency

Cost optimization (AUCs)

Each cycle of Devin consumes ACUs. Experienced engineers save dozens ACUs just writing Better specs and validation locally before delegating.

Golden rule: everything your laptop can validate (lint, syntax, ownership), validate before asking Devin.

Tip #1

Avoid fixed loops

If Devin tries to fix the same 2x error without progress, stop. Refresh Spec.

Tip #2

No gesswork

Provide field names, paths and examples. Guessing costs a lot.

Tip #3

Lint local first

Rhode prettier, eslint and validation scripts before the agent.

Tip #4

Focused Sessions

A spec per session. Avoid "while you're at it..." — it becomes scope infinite.

Tip #5

Use Blueprints

Pre-configured environment prevents resettlements every session.

Tip #6

Rich knowledge-base

Inventory, ownership and documented pipeline reduce questions and mistakes.

Pipeline tip by tip

The Workflow

From Spec to Deploy in Production, no dangerous shortcuts. This is the canonical sequence.

  1. 1

    Spec Creation

    Time/Tech Lead describes demand in specs/<ticket>.md following the SDD template.

  2. 2

    Environment setup

    Run bootstrap scripts locally.

    # Setup local + autenticação Web
bash scripts/environment/setup.sh
bash scripts/environment/authenticate-orgs.sh
sf org login web --alias qa
  3. 3

    Development via Devin

    Local (Devin CLI) or Web. Devin reads Spec, runs playbooks and code in force-app/.

  4. 4

    Validation & testing

    Check-only in sandbox + ownership and destructive validations change.

    python scripts/validation/check-metadata-ownership.py
python scripts/validation/check-destructive-changes.py
sf project deploy start --check-only --target-org qa
bash scripts/salesforce/run-tests.sh
  5. 5

    Synchronization via Flosum CLI

    Package package-deploy.xml enters the Flosum, who takes care of the promotion QA → PreProd → Prod.

    # Apenas após PR aprovado e merge na main
npx @flosum/cli branch sync --branch <feature-branch>
npx @flosum/cli package deploy \
  --manifest manifest/package-deploy.xml
    Devin no runs this step in shared environments — Flosum is the deployer Officer.

Repository

Repository structure (reference model)

The repository Github. with/issei/sf-repo It's the official template. Use it as a starting point (fork or clone) — so playbooks and essential skills They're already preconfigured to orchestrate Devin immediately. 

sf-repo/
├── CLAUDE.md            # Instruções base ao agente
├── .agents/
│   └── skills/          # Componentes modulares reutilizáveis
├── knowledge-base/     # A "memória" do Devin
│   ├── metadata-ownership.yaml
│   ├── org-inventory.md
│   └── flosum-pipeline-map.md
├── playbooks/          # SOPs determinísticos
│   ├── 01_setup.md
│   ├── 02_apex.md
│   └── 03_promotion.md
├── scripts/            # Ferramentas locais (bash/python)
│   ├── environment/
│   ├── salesforce/
│   ├── validation/
│   └── reporting/
├── specs/              # Demandas de negócio (entrada principal)
│   └── TICKET-123.md
├── force-app/          # Código Salesforce
└── manifest/
    └── package-deploy.xml
/knowledge-basepersistent context

Org inventory, pipeline map Flosum and ownership by domain. It's the first thing Devin reads.

/playbooksdeterministic execution

Step by step. When the agent references a Playbook, it follows a tested SOP.

/specsmain entry

Where the demand lives. Spec is Devin input #1 for any session.

/.agents/skillsreuse

Modular Skills that the agent combines for recurring tasks (e.g. generate Apex tests, validate package, generate report).

Adopting the template

  1. Do it. fork of issei/sf-repo for your team.
  2. Update knowledge-base/metadata-ownership.yaml with the domain of Team.
  3. Configure orgs in org-inventory.md and pipeline in flosum-pipeline-map.md.
  4. There you go: Devin has already understood its context since the first session.

Governance

Ownership, borders and checklist

In a federated model, metadata ownership is sacred. Violating a border = methadata trampling = Deploy broken for another team.

Who can change what

Custom Objects & Fields

Only the domain team owner (e.g. CommercePricing).

Apex Classes

Team owner; naming conventions by logical namespace.

Flows

Only after cross impact validation between teams.

Profiles & Layouts

Components shared — Flosum Smart Merge Compulsory.

Isolation strategies

  • ● Ownership by domain declared metadata-ownership.yaml.
  • ● Naming conventions with time/domain prefix (e.g. CP_OrderTrigger).
  • ● Namespace of course separating classes, objects, layouts and LWCs.
  • ● Validation automated via check-metadata-ownership.py before PR.
Consequence of the violation: methadata trampling — another team discovers, in a future deploy, that its components have been overwritten. Result: emergency rollback, rework and loss of confidence in the pipeline.

Developer Checklist

interactive

Mark as you go — it works as a quick guide.

Who's behind

Authority, real context and learning practical

This framework was not born on slide. It was distilled from real experiments with teams Salesforce, autonomous agents and pipelines Flosum in production. What's here is what worked -- and what cost money. to find out.

Real context

Where this framework was forged

  • ▸
    Multiple teams sharing QA, PreProd and Prod, each with Your methadata domain.
  • ▸
    Relevant Legacy: triggers and live streams for years, with hidden dependencies.
  • ▸
    Devin in production, reading real Specs, running playbooks and opening PRs.
  • ▸
    Flosum as SoT, with Smart Merge solving overlaps between Teams.

Learning

What it took to find out

  • ✓
    Every ambiguous Spec becomes rework — always. There is no long prompt that compensates for loose Spec.
  • ✓
    Ownership by domain is the only real defense against methadata trampling.
  • ✓
    Blindly trusting the agent costs double: in ACUs and rollback.
  • ✓
    Rich knowledge-base reduces average number of iterations from 5 to 1–2.
  • ✓
    Flosum is the promoter. Always. No exceptions.

Where this content is positioned

Most content on AI + development focuses on magic prompts. This approach focus on engineering: contracts, borders, validation, governance. It's what differentiates predictable delivery from "pretty demo that breaks on Thursday." If you're Tech Lead, Architect or Senior in Salesforce, it is a process that has worked in this context — and is tested in the field.

The Future

From encoders to Agent Tech Leads

The future of Salesforce is not to type XML faster. It's orchestrating Specs, Skills and agents to deliver business value with real governance. The tooling is ready. The next choice is yours.

Official links

Devin

Devin CLI · Documentation

cli.devin.ai/docs

Devin

Blueprints

docs.devin.ai/onboard-devin/environment/blueprints

Devin

Blueprint Reference

docs.devin.ai/.../blueprint-reference

Devin

Agents. md

Docs. devin.ai/onboard-devin/agents-md

Flosum

Flosum CLI (Node.js)

docs.flosum. with/.../flosum-cli-node.js-package

Salesforce

Salesforce CLI (sf)

Github. with/salesforcecli/cli

Salesforce

Web Login (auth flow official)

devoloper. Salesforce. with/.../sfdx dev auth auth web flow.htm

Template

issei/sf-repo (reference)

Github. with/issei/sf-repo

Audio deepening

Debate: ed transition to orchestrator of AI agents

A 22-minute debate that strengthens and deepens the whole idea presented in this material — the change of mentality from "who type code" to "who orchestra agents", applied to the Salesforce context with SDD, Devin and Flosum.

Single episode · 22 min

The transition to orchestrator of AI agents

Auditory reinforcement of the central thesis of this portal.

Download audio (.m4a) Open in new tab

In short

Develop Salesforce with AI without technical debit

Use AI in Salesforce without accumulating technical debt requires method: AI implements against a versioned specification, not against loose prompts, and each change goes through governance and automatic validation before deploy.

  • Spec-Driven Development — the spec is the source of the truth; Devin implements against her.
  • Federated Model — autonomy of squares with central governance via Flosum CLI.
  • Automatic validation — Apex Tests and gates prove intention before promoting.
  • Salesforce CLI + Git — the state of a versed and auditable metadata.

Operationalizes the principles of AI Agent Engineering.

Frequently Asked Questions

Frequently Asked Questions

How to use AI in Salesforce without generating technical debit?

Making the IA implement against a versioned specification (Spec-Driven Development) instead of loose prompts, and validating each change with Apex Tests and Gates before deploy. Technical debt is born from generating code without contract; the method changes "torça to function" for auditable intent.

What is the Federated Development Model?

It is an arrangement that gives autonomy to the squares to deliver quickly, maintaining central governance on quality and security (via Flosum CLI and common policies). Decentralizes execution without decentralising risk.

What's Devin's role in this flow?

Devin is the agent that executes the implementation against spec: discovers the relevant context, edits the metadata/Apex and runs the tests. The human acts as Cognitive Orchestrator, defining intention and judging the result.

How does Flosum and Salesforce CLI get in?

Salesforce CLI reviews the state of metadata in Git, making each change traceable; Flosum CLI applies governance (approvals, policies, promotion between environments) on this treadmill. Together they guarantee speed with control.