Context that follows the task
Connect the records, documents, and conversation history needed for a particular job. Keep unrelated data and permissions outside the task.
AI Agent Orchestration / Moon Sherpa LabsMOON SHERPA / LABS
AI agents that move
work forward.
For teams ready to move from a promising AI demo to a dependable part of the working day. We connect the context, tools, and review steps around a clearly defined outcome.
Agent orchestration / A working score
Give every role a reason to exist.
Parallel work helps when the tasks are independent. Shared context, reconciliation, and review make the combined result useful.
SUBJECT STUDY / 04
An illustrative research workflow. Lane lengths express structure, not measured execution times or performance claims.
THE DECISION IN VIEW
Separate tasks. One shared brief.
Research, source checking, and preparation can contribute different evidence to the same brief. Each role needs a bounded responsibility and a useful output contract.
- Start with
- A shared goal and bounded tasks
- Make possible
- Contributions ready to reconcile
More agents are useful only when the work can benefit from the separation.
The opportunity
Turn a request into a finished piece of work.
The useful question is specific: can this system take an incoming request, gather the missing context, prepare the right action, and leave a clear record of the result?
We start there. Then we decide which steps need rules, which benefit from a model, and which belong with a person. Multiple agents are useful when the work genuinely separates into independent responsibilities; a simpler workflow is often the better starting point.
What we can build
The pieces around the intelligence.
Tools with a clear purpose
Expose the actions the workflow actually needs through APIs or MCP. Give each operation understandable inputs, results, and failure behavior.
Coordination without guesswork
Separate work into stages with explicit inputs and completion criteria. Run independent tasks together where that improves the experience.
A place for exceptions
Define what happens when a source is unavailable, an answer is incomplete, or a person needs to decide. Make those cases visible to the team.
The decision, made clearer
Choose the mechanism for each step.
| The work | A useful starting point | What we verify |
|---|---|---|
| A known trigger and a predictable update | A deterministic workflow | Duplicate events, retries, and the final record |
| Reading or classifying varied material | A model with structured output | Representative examples and clear acceptance criteria |
| Independent research or preparation tasks | Coordinated agents | Bounded responsibilities and a reliable handoff |
| An external commitment or sensitive decision | A review checkpoint | Who approves, what they see, and what is recorded |
A good starting point
A repeated workflow with useful source material, an identifiable owner, and an outcome your team can recognize as complete.
A boundary worth discussing
A loosely defined goal, inaccessible source systems, or an expectation that the model will make every judgment without supervision needs more discovery first.
Working together
Prove one complete workflow first.
Map the real job
Walk through a representative request with the people who do the work, including the awkward exceptions.
Define the evaluation
Collect examples and agree what a useful result looks like before optimizing model behavior.
Build the complete path
Connect the inputs, tools, review steps, and final destination in a working version your team can try.
Release with visibility
Roll out within an agreed scope, inspect results, and use the evidence to choose the next improvement.
In practice
A working reference: Follow Up Ace.
Our CRM product connects context to a next action across an embedded assistant, MCP clients, and a mobile companion. The project is a concrete reference for tool access and reviewed work.
AI Platform · CRM Intelligence
Follow Up Ace
An AI layer for Follow Up Boss that ranks the day and prepares personal follow-up drafts, with a shared assistant across the CRM, Claude, ChatGPT, and an iPhone companion.
Inside the buildExplore the working pattern
Follow a request from context to review.
Run an illustrative CRM workflow, remove a source, and inspect the prepared draft. The system pauses when information is missing and waits for a person before the handoff.
Workflow laboratory / 04
The work, in motion.
Illustrative workflow
No live customer data
Ready when you areSYS / 00
Inside the workflow01 / 05
A useful starting point
A conversation worth continuing
Signal
A fictional buyer asked about a garden and a quieter street. Their agent opens the follow-up queue.
Run the scenario to follow the whole path, or take it one step at a time.
See the real work: Follow Up AceTry disconnecting the source to see how missing information changes the path.
Before we begin
The useful questions.
What is AI agent orchestration?
It is the coordination of model-driven tasks, tools, and handoffs around an outcome. It can involve one agent or several specialists. The important design work is deciding what each part can do, what counts as complete, and when a person takes over.
How is it different from a chatbot?
A chat interface can be part of an agent system. The distinction is the workflow behind it: access to relevant tools, state across steps, execution rules, and a defined result. A conversational interface alone does not tell you how much work the system can perform.
Do we need to replace our existing software?
Usually the first step is to inspect what your existing systems expose through APIs, exports, and supported integrations. We build around the tools that already fit the business and identify any limits before promising a connection.
Does every project need multiple agents?
No. A single model step inside a conventional workflow may be enough. Separate agents make sense when tasks have distinct responsibilities or can usefully run independently.
Can our team approve actions before they happen?
Yes. Review checkpoints are part of the workflow design. We agree which actions can run automatically, which prepare a draft, and which require explicit approval.
How are cost and timing scoped?
The estimate follows the workflow: systems to connect, data quality, evaluation needs, review requirements, and deployment responsibilities. We define a useful first release before committing to an implementation plan.
Do we need an orchestration platform, or a custom-built system?
A platform can be the right call when your workflow fits its supported connectors and review model out of the box. A custom-built system is worth the extra scoping when the handoffs, approval logic, or data connections are specific enough that a platform's defaults don't match how your team actually works.
What should we look for in an AI agent development company?
Evidence of production deployments, not just demos—ask what breaks in practice and how they handle it. Confirm they design explicit review checkpoints rather than full autonomy by default, and that they'll show you the actual tool-call logs, not just a polished dashboard.
Let’s make it concrete
Bring us one piece of work that should move more easily.
A real request, the tools involved, and the point where someone gets stuck are enough to start.
Map an agent workflow