Multi-agent orchestration that transforms how enterprise Salesforce is built: from ticket to deployed code, with calibrated human oversight at every step.
This is not a code generator. It's a development workflow that orchestrates AI agents across the full lifecycle: ticket evaluation, brownfield discovery, implementation, testing, review, and knowledge capture. The human controls the collaboration intensity. The system compounds what it learns.
Each step is non-blocking: the developer can import tickets, run evaluations, and launch discovery on multiple items in parallel. The kanban board shows real-time status across all active work.
Before any code is written, the system evaluates each ticket against the AI agent's capabilities and the target org's complexity. An LLM analyzes the requirements, acceptance criteria, and brownfield state of the connected sandbox to produce a confidence score that determines the collaboration mode.
The developer can refine the score by answering targeted questions about ambiguities, scope, and risk. Higher confidence unlocks more autonomy; lower confidence triggers more checkpoints.
Across all modes, the agent never resolves ambiguous requirements by default. When a ticket allows more than one valid interpretation, the agent documents the ambiguity, proposes options, and waits for the developer's decision.
On highly customized Salesforce instances, the cost of breaking existing automation is high. Discovery is mandatory: the agent evaluates the current state of the org before writing any code.
Agent connects to the target sandbox via Salesforce MCP or CLI, retrieves metadata for objects, flows, triggers, and classes in the ticket's scope. Documents what exists and flags unexpected dependencies.
Optionally runs existing Apex tests covering the affected area. Documents pass/fail results and coverage gaps. If many tests fail, the agent halts and waits for the developer's decision before proceeding.
Produces a structured discovery artifact stored on the backlog item: current state, risks, constraints, and test results. If scope needs adjustment, the agent proposes refined acceptance criteria or new child tickets.
The workflow generates a structured prompt from the Jira ticket, confidence evaluation, and discovery artifact. The developer pastes it into a Cursor agent session, and the AI begins implementation.
The agent works locally in the IDE and directly in the Salesforce sandbox via the Salesforce MCP server and CLI, using the developer's OAuth credentials. The developer sees every action, approves tool usage, and provides input at checkpoints determined by the collaboration mode.
When the AI agent completes implementation, the ticket moves to Review automatically. The review panel shows a structured summary of everything that was delivered: code, configuration, tests, and Apex coverage.
The developer logs into the sandbox to verify, run additional tests, or make adjustments. From the review panel, they can either request changes (with instructions for the AI agent) or approve and check in the ticket to Jira.
Check-in updates the Jira ticket status, adds session notes and deliverable details as a comment, and attributes the work to the developer via 3LO OAuth.
Two sources of learning feed a persistent lessons database after every ticket: what was discovered about the org (constraints, test gaps, dependencies) and what was learned during development (guardrail suggestions, decisions, warnings). Future work on related areas automatically receives this context.
What exists in the org, what constraints were found, what tests cover (or don't), and what risks to watch for. Stored on the backlog item and ingested at review.
Guardrail suggestions, knowledge captured during implementation, decisions made, and warnings for future work. Extracted from the agent's session reflection.
Lessons are tagged by project, workflow type, and scope. When a future ticket touches a related area, the agent automatically receives prior context: "what we already know about this area."
A development workflow that orchestrates AI agents across the full Salesforce lifecycle: from Jira ticket through brownfield discovery, confidence-calibrated implementation, structured review, and knowledge capture that compounds across the team.
The developer stays accountable. The system gets smarter with every session.