AI Agent Orchestration for RevOps: Connecting Multiple Agents Into a System

You built an enrichment agent. Then a routing agent. Then a deal inspection agent and a pipeline alert system. They each work well in isolation, but they do not talk to each other. The enrichment agent fills in firmographic data, but the routing agent does not know enrichment just completed and uses stale data instead. The deal inspection agent flags missing stakeholders, but the alert system fires a separate notification about the same issue. This is the orchestration problem, and solving it is the difference between having a collection of tools and having an intelligent system.

The Case for Orchestration¶

Individual agents solve individual problems. Orchestrated agents solve workflows. Here is what changes when you connect them:

The Orchestration Architecture¶

A RevOps agent orchestration system has four layers:

1. Event bus. A central message queue (Kafka, RabbitMQ, or even a simple Redis pub/sub) where agents publish events and subscribe to triggers. When the enrichment agent completes a record update, it publishes an “enrichment.complete” event. The routing agent subscribes to that event and immediately processes the enriched record.

2. Shared context store. A lightweight database or state store where agents read and write shared context. This prevents each agent from independently querying the CRM and ensures they all work from the same snapshot. The context store holds:

• Current record state (as enriched by upstream agents)
• Agent action history (what each agent has done to this record)
• Pending flags and unresolved issues
• Priority and SLA metadata

3. Orchestration controller. The brain of the system. It manages:

• Workflow definitions: Which agents run in what order for each event type
• Dependency resolution: Agent B waits for Agent A to complete before starting
• Conflict resolution: If two agents want to update the same field, the controller decides which write wins based on confidence scores and priority rules
• Circuit breakers: If an agent fails repeatedly, the controller stops sending it work and alerts the ops team

4. Governance layer. Rules that apply across all agents:

• Maximum number of CRM writes per record per hour (to prevent update storms)
• Required confidence thresholds for auto-writes vs. human review
• Audit logging requirements
• Data sensitivity rules (e.g., agents cannot auto-modify records with active legal holds)

Designing Agent Workflows¶

Here are two common orchestrated workflows:

New lead workflow: 1. Lead created in CRM (trigger) 2. Enrichment agent fills firmographic and technographic fields 3. Scoring agent calculates ICP fit and intent score 4. Routing agent assigns the lead to the best-fit rep 5. Alert agent notifies the rep via Slack with a prospect brief 6. Total elapsed time: under 90 seconds

Deal review workflow: 1. Daily cron trigger at 6:00 AM 2. Deal inspection agent evaluates all active opportunities 3. Forecasting agent recalculates win probabilities for flagged deals 4. Alert agent sends risk summaries to reps and a rollup dashboard to managers 5. Comp audit agent validates that flagged deals are not affecting payout calculations incorrectly

Handling Cascading Failures¶

The biggest risk in orchestration is one agent’s error propagating downstream. Implement these safeguards:

• Validation checkpoints. Between each agent handoff, validate that the output meets expected schema and value constraints. If the enrichment agent returns an employee count of -1, the checkpoint catches it before routing uses it.
• Rollback capability. Every agent write should be reversible. Store the original value alongside the new value so the system can undo a chain of bad updates.
• Dead letter queue. Records that fail validation go to a quarantine queue for human review instead of silently breaking downstream agents.

Governance principle: Start with loose coupling. Let agents communicate through the event bus, not by calling each other directly. This makes it easy to add, remove, or replace individual agents without rewriting the entire system.

Choosing Orchestration Tools¶

You do not need to build the orchestration layer from scratch:

• LangGraph / CrewAI - Purpose-built for multi-agent coordination with built-in state management
• Temporal / Prefect - Workflow orchestration platforms that handle retries, timeouts, and state
• Custom Python + Redis - Lightweight option for teams with fewer than five agents
• n8n / Make - Low-code options for teams without dedicated engineering resources

Measuring Orchestration Health¶

Track these system-level metrics alongside individual agent metrics:

• End-to-end latency - Time from trigger event to final action across the full workflow
• Checkpoint pass rate - Percentage of handoffs that pass validation without intervention
• Conflict rate - How often two agents attempt conflicting writes on the same record
• System uptime - Availability of the event bus and orchestration controller
• Human intervention rate - What percentage of records require manual review in the dead letter queue

Key Takeaways¶

• Orchestration transforms a collection of individual agents into a coordinated system that handles end-to-end workflows
• The architecture requires four layers: event bus, shared context store, orchestration controller, and governance
• Cascading failures are the primary risk - mitigate with validation checkpoints, rollback capability, and dead letter queues
• Start with loose coupling through an event bus so agents can be added or replaced independently
• Measure end-to-end latency and checkpoint pass rate to monitor system health beyond individual agent performance