How to orchestrate people, agents, and bots in complex workflows
Modern enterprises are running workflows across humans, AI agents, and software bots simultaneously. The challenge is not automation itself. The challenge is coordination.
You know the scenario. A document arrives, but who acts first? A bot flags an exception, but does it route to an AI agent or a human reviewer? A deadline approaches, but how does the system know to escalate before a breach occurs?
These questions point to a gap most automation strategies ignore. According to McKinsey's 2025 State of AI report, 88% of enterprises now use AI in at least one function, yet only 33% successfully scale their automation programs.
The missing piece is orchestration: the system that decides who or what should act next, manages queues of pending work, and enforces service level expectations across every stage.
This guide breaks down how to build that orchestration layer so your workflows stay responsive, auditable, and aligned with business priorities.
Key takeaways
Orchestration coordinates three distinct actors. Humans provide judgment and exception handling, AI agents provide context-aware decisions, and bots handle repetitive execution. Each plays a defined role in a larger workflow network.
Event triggers determine what happens next. Real-time events like document arrivals, threshold breaches, or schedule triggers act as the logic points that route work to the right actor at the right time.
Queues prevent work from falling through the cracks. Holding areas for pending tasks ensure nothing gets misrouted or overlooked, while giving teams visibility into workload flows and bottlenecks.
SLA management shifts from reactive to proactive. Modern orchestration predicts possible breaches and reassigns tasks before deadlines, rather than simply alerting after failures occur.
What people, agents, and bots actually do in workflows
The first step to effective orchestration is understanding what each actor brings to the table and where their capabilities end.
People (humans) remain essential for judgment, context interpretation, and high-stakes decisions. When a compliance flag requires nuanced understanding or a client relationship needs a personal touch, automation cannot substitute for human expertise. The pain point? Humans are expensive and slow for repetitive work. They add the most value when reserved for ambiguity and exception handling.
Agents (AI and autonomous software) represent a newer category of workflow participants. Unlike rule-based bots, agents can observe, reason, and adapt. Microsoft's AI Agent Orchestration Patterns documentation describes how modern agents handle specialization (each agent focuses on a specific domain), scalability (agents can be added without redesigning the system), and maintainability (testing can focus on individual components). Agents excel at triage, classification, and context-aware routing decisions.
Bots (task automators and RPA) execute predefined tasks based on rules or scripts. They do not reason autonomously, but they deliver speed and consistency for structured, high-volume work. Data extraction, form population, and system-to-system transfers are their domain.
Appian's workflow orchestration guide captures the core principle: orchestration treats each actor as a role in a larger network where humans add judgment, bots add speed, and agents add context-aware decisioning. When roles are clearly defined, workflows become efficient and auditable.
With Moxo, teams define these roles within visual workflow templates that specify exactly when tasks route to humans, when AI agents handle review, and when bots execute handoffs. The platform's workflow automation capabilities let you configure the logic for each actor type, including automatic escalations when tasks sit unattended.
For client-facing processes, Moxo's white-labeled client portals extend orchestration beyond internal teams. Clients see a branded experience while the system routes their submissions through the same event-driven logic governing your internal workflows.
How events and queues steer complex workflows
Orchestration systems operate on event-driven logic. Events act as the switches that determine what happens next.
The pain point with traditional automation is rigidity. Workflows run on fixed schedules or require manual triggers, creating delays when conditions change. Event-driven systems respond in real time. Superblocks' workflow orchestration guide defines event triggers as mechanisms that start workflows based on real-time events such as a file upload, a system alert, or a threshold breach.
Consider a practical example: a document arrives in a client portal. This event triggers parsing. A bot extracts structured data. An AI agent evaluates extraction confidence. If confidence falls below a threshold, the system queues the document for human review. No manual intervention required to route the exception.
Queues serve as holding areas for pending work waiting for assignment. Humans queue exception reviews. Bots queue routine tasks. Agents queue analytical decisions. This architecture ensures no work gets lost, misrouted, or overlooked.
The ROI lever here is visibility. When queues are transparent, teams can identify bottlenecks before they cascade. When events trigger automatic routing, response times shrink.
Moxo's workflow automation interprets these event triggers in real time, ensuring the correct entity receives the work. The platform's real-time notifications prompt humans at exactly the right moment, whether that is an approval request, a document requiring review, or a deadline approaching. Escalations happen automatically based on queue state or deadline proximity, so nothing sits idle.
For teams already using CRM or ERP systems, Moxo's third-party integrations connect workflow events to your existing tools. When a deal closes in Salesforce or HubSpot, that event can trigger onboarding workflows automatically. Document uploads sync with Dropbox or Box. The orchestration layer sits on top of your existing stack rather than replacing it.
Why SLA management matters for human-in-the-loop systems
Service Level Agreements anchor expectations for timeliness across workflows involving humans, bots, and agents. The problem? Traditional SLA tracking is reactive. Dashboards highlight breaches after they have occurred, and teams scramble to explain why.
Newgen's analysis of AI-driven SLA management describes the shift: AI agents introduce a fundamental change from measuring SLA adherence after the fact to maintaining it in real time. They do not just monitor. They orchestrate, predicting deviations and executing corrective actions autonomously.
This matters because hybrid workflows create SLA complexity. A bot might complete its step in seconds, but if the human reviewer takes three days, the overall SLA fails. Proactive SLA management addresses this by predicting possible breaches, reassigning or escalating tasks before deadlines, and adapting queue priorities dynamically.
Moxo's SLA-aware monitoring lets architects define criteria for each workflow stage and automatically surfaces breach risks within dashboards. The approvals engine handles multi-stage sign-offs with configurable routing, so complex approval chains do not become bottlenecks. When a stage approaches its deadline, automated reminders notify the responsible party before escalation becomes necessary.
Every interaction is tracked through Moxo's audit trails, creating timestamped records of who did what and when. For regulated industries, this compliance tracking is not optional. It is the difference between passing an audit and scrambling to reconstruct what happened.
Best practices for orchestrating humans, bots, and agents
Effective orchestration requires deliberate architecture choices. Here is what the most successful implementations get right.
Define clear roles for each actor. Specify which decisions require human judgment, which benefit from AI reasoning, and which should run as automated bot tasks. Overlap creates confusion and audit gaps. Document these roles so every team member understands the logic.
Use event-driven logic to trigger handoffs. Static schedules cannot adapt to real-time conditions. Events like document arrivals, approval completions, or threshold breaches should automatically route work to the next actor. This is where orchestration becomes responsive rather than rigid.
Monitor queues and bottlenecks with real-time dashboards. Visibility into pending work across all actor types enables proactive load balancing before delays compound. If one reviewer consistently has a backlog while others sit idle, you can redistribute before SLAs suffer.
Apply SLA governance that predicts and prevents. Configure systems to escalate or reassign tasks before deadlines, not after breaches occur. The goal is proactive intervention, not reactive damage control.
Log all interactions for observability and compliance. Every handoff between humans, agents, and bots should create an auditable record with timestamps and context. This is non-negotiable for regulated workflows and invaluable for process improvement.
Moxo unifies these practices within a single orchestration layer. The workflow builder enables visual event logic and queue definitions. AI agents handle review and triage. All events and handoffs are logged with timestamps for governance through built-in audit trails and security.
Moxo: The orchestration layer that coordinates people, agents, and bots
Most automation stacks already have the components. Bots execute tasks. AI agents classify, score, and recommend. Humans review, approve, and resolve exceptions. What’s missing is the connective tissue that governs how work flows between them in real time.
Moxo fills this orchestration gap by acting as the human + AI-assisted workflow layer that sits above individual actors, coordinating handoffs based on events, queue state, and SLA conditions.
At its core, Moxo is not trying to replace bots, agents, or humans. It defines when each should act, what context they receive, and what happens next if work stalls, changes, or escalates.
Event-driven coordination across all actors
Moxo workflows are built around real-time events rather than static sequences. When a document is uploaded, a form is completed, a deadline approaches, or a confidence threshold is breached, Moxo routes the task to the appropriate actor automatically. Bots can execute structured steps, AI agents can assess or triage, and humans are pulled in only when judgment is required.
This prevents the common failure mode where automation breaks and exceptions fall out into email or ticketing systems with no clear ownership.
Queue-based workload management with visibility
Instead of burying work inside system logs or siloed dashboards, Moxo maintains explicit queues for humans, agents, and automated steps. Every task has a visible state: waiting, in review, approved, escalated, or completed.
This queue-based architecture gives teams real-time visibility into bottlenecks and workload distribution. When one reviewer is overloaded or a task sits idle too long, the orchestration layer can rebalance or escalate automatically, before SLAs are missed.
SLA-aware orchestration, not after-the-fact reporting
Moxo’s SLA logic is embedded directly into workflows. Architects can define time expectations at each stage, whether the task is handled by a bot, an AI agent, or a human reviewer. The system monitors progress continuously and triggers reminders, reassignments, or escalations as deadlines approach.
This shifts SLA management from reactive dashboards to proactive intervention, which is essential in hybrid workflows where a single delayed human step can derail an otherwise fast automated process.
Extending orchestration beyond internal teams
A critical differentiator is that Moxo’s orchestration model extends to external stakeholders. Clients, partners, and third parties participate through secure, white-labeled workspaces that operate under the same event logic, queues, and SLA rules as internal users.
Instead of breaking orchestration by emailing clients for missing information, organizations route those exceptions directly into shared workspaces with full context. Clients see exactly what is required, upload documents, respond to requests, and move the workflow forward without leaving the system.
Auditability across every handoff
Every event, task assignment, decision, and escalation in Moxo is logged with timestamps and user context. This creates a continuous audit trail across humans, agents, and bots, critical for regulated industries and invaluable for process optimization.
Rather than reconstructing workflows from disparate logs, teams get a unified record of how work moved, who acted, and why decisions were made.
In practice, Moxo becomes the control plane for complex automation: coordinating execution, enforcing accountability, and preserving context across every actor involved.
“Moxo streamlines the onboarding process by keeping everything, documents, tasks, and communication, in one place. It eliminates the back-and-forth emails and helps everyone stay aligned on what needs to happen next.” - G2 reviewer, Financial Services
This speaks directly to orchestration outcomes: fewer handoff failures, clearer ownership, and faster resolution when humans, systems, and external stakeholders must work together.
Conclusion: Orchestration that actually works
Orchestrating people, agents, and bots is not about replacing one with another. It is about designing intelligent, event-driven workflows where each actor contributes their strengths. Queues ensure nothing falls through the cracks. SLA governance keeps work moving before deadlines become crises. Clear handoff protocols maintain context and auditability across every stage.
Moxo provides the orchestration layer that connects humans, AI agents, and systems within a single platform. With visual workflow design, multi-stage approvals, AI-powered automation, and built-in audit trails, teams can build workflows that adapt to real-time conditions while maintaining full compliance tracking. White-labeled portals extend orchestration to client-facing processes, and native integrations connect your existing CRM and business systems.
Get started with Moxo to orchestrate your workflows across every actor type.
FAQs
What does orchestrating people, agents, and bots mean?
Orchestrating people, agents, and bots refers to coordinating humans, AI agents, and software bots so workflows are executed efficiently across predefined logic, events, and queues. Each actor handles tasks suited to their capabilities while a central orchestration layer manages handoffs and priorities.
How does SLA management work in human-in-the-loop workflows?
SLAs define expected completion times for each workflow stage. Modern orchestration systems monitor queues in real time and generate escalation or reassignment actions before breaches occur, shifting from reactive alerts to proactive intervention.
What is the difference between a bot and an agent?
Bots execute predefined, rule-based tasks and do not reason autonomously. Agents have decision logic, can observe context, and adapt their actions accordingly. Both play distinct roles in orchestration: bots handle structured execution while agents handle triage and context-aware routing.
Why use event-driven logic in orchestration?
Events trigger actions, queue moves, or handoffs dynamically based on real-time conditions like document arrivals or threshold breaches. This makes workflows responsive and resilient compared to static schedules that cannot adapt to changing circumstances.
Can Moxo integrate with existing CRM and ERP systems?
Yes. Moxo connects to major platforms, including Salesforce, HubSpot, Dropbox, Box, and Slack, through native connectors, APIs, and webhooks. Teams can route data in and out of the orchestration layer while keeping files synchronized without manual uploads. See all integrations.
How long does implementation take?
Most teams launch a focused pilot within days and expand over a few weeks as workflows get refined. Simple use cases like document collection and status updates go live quickly with templates, while complex multi-party approvals require additional time for mapping steps and permissions.
