TIER-01Isolated
Single Agent
A single AI agent handles one task: extract data from a PDF, generate a customer summary, or process a payment request. It works in isolation, with clear inputs and outputs.
SHEET 01Technical GuideORCHESTRATION
The Complete Guide to Multi-Agent Orchestration
Coordinate dozens of AI agents across departments without chaos. Learn the patterns, architecture decisions, and governance frameworks that separate successful multi-agent systems from failure modes.
SHEET 02FoundationsTIER-01..03
What Is Multi-Agent Orchestration?
The difference between individual agents and coordinated systems.
TIER-02Isolated
Multi-Agent System
Multiple specialized agents work on interconnected tasks. Agent A extracts data; Agent B validates it; Agent C applies business rules; Agent D executes actions. Coordination is implicit.
TIER-03Governed
Orchestration
Active coordination of multiple agents. The orchestrator routes tasks, manages dependencies, resolves conflicts, enforces permissions, and reports outcomes. Coordination is explicit and governed.
Why this matters
Orchestration prevents chaos. Without it, agents collide, create infinite loops, accumulate excessive permissions, and leave no audit trail. With it, you can deploy dozens of agents that work reliably at scale.
SHEET 03ArchitecturePAT-01..04
Orchestration Patterns
Four proven patterns for coordinating multiple agents.
PAT-01Pattern
Sequential Pipeline
Agent outputs feed directly into the next agent in sequence. Ideal for linear workflows with defined handoffs.
Example
Invoice extraction → validation → approval → payment processing
PAT-02Pattern
Parallel Fan-Out
Multiple agents process the same input simultaneously. Results are merged and consolidated by a coordinator.
Example
5 agents analyze credit risk, market exposure, operational risk, regulatory compliance, and financial health in parallel
PAT-03Pattern
Supervisor Pattern
A manager agent evaluates tasks and delegates to specialist agents. Synthesizes results into final decisions.
Example
Executive agent routes customer issues to sales, support, or retention agents based on context
PAT-04Pattern
Event-Driven Mesh
Agents react independently to events in a shared event stream. Coordination happens through state and rules.
Example
When a contract is signed, notifications trigger billing, onboarding, and compliance agents autonomously
SHEET 04RiskFAIL-01..03
Why Orchestration Fails Without Governance
Three critical failure modes and how to prevent them.
FAIL-01Fault
Agent Conflicts
Two agents attempt to update the same resource simultaneously. Without locking or versioning, data corruption occurs.
Real scenarioAgent A and Agent B both try to update a CRM account record. Whichever writes last overwrites the other.
FAIL-02Fault
Runaway Chains
Agent A triggers Agent B, which triggers Agent C, which re-triggers Agent A. Loop guards and depth limits are essential.
Real scenarioAn approval agent requests more info from a document extraction agent, which re-submits the document, creating an infinite loop.
FAIL-03Fault
Permission Drift
Agents accumulate access far beyond what they need for their task. Unused permissions become security risks.
Real scenarioAn HR onboarding agent gains IT permissions to provision systems, then never loses them when task scope changes.
SHEET 05Platform CapabilitiesAS-BUILT
How assistents.ai Orchestrates
Comparison with DIY approaches and legacy RPA systems.
Capabilityassistents.aiDIY (LangChain/CrewAI)Legacy RPA
Agent RoutingIntent-aware routing with policy enforcementManual routing via conditional logicFixed workflows; no dynamic routing
Conflict ResolutionAutomatic locking, versioning, transaction rollbackCustom conflict detection (error-prone)Requires manual intervention
Permission EnforcementReal-time scope enforcement, per-agent policiesRelies on careful codingStatic access lists
Audit Trails100% immutable logs of every agent actionOptional logging; fragmented across toolsLimited or no audit capability
RollbackAtomic rollback of workflow or single agent actionManual recovery via snapshotsTime-consuming manual fixes
ScaleHundreds of agents, zero coordination overheadCoordination complexity grows exponentiallyHard limits on number of workflows
SHEET 06Case Study5-AGENT
Real-World Multi-Agent Workflow
How orchestration manages a new hire onboarding process.
A new hire is approved. The orchestrator activates a 5-agent workflow. Each agent has specific responsibilities; none overlap. Dependencies are explicit.
AGT-01
HR Agent
Specialist agent
- Creates employee record in HRIS
- Provisions benefits enrollment
- Schedules orientation
AGT-02
IT Agent
Specialist agent
- Provisions laptop and hardware
- Creates domain accounts and access groups
- Configures VPN and security tools
AGT-03
Finance Agent
Specialist agent
- Sets up payroll in accounting system
- Issues corporate expense card
- Configures cost center allocation
AGT-04
Manager Agent
Specialist agent
- Schedules first 1:1 meeting
- Assigns team onboarding tasks
- Shares team handbook and resources
CTRL
Orchestrator
Coordination layer
- Waits for HR agent to complete before IT can provision
- Delays payroll setup until Finance reviews contract
- Detects if any agent fails and escalates
- Reports status dashboard to hiring manager
Orchestration value
The orchestrator waits for HR to complete before IT provisions (new employee data is required). It delays payroll setup until Finance reviews the employment contract (compliance). If any agent fails, it escalates and rolls back dependent steps. The entire workflow is logged and auditable.
SHEET 07Operating MetricsMEASURED
Orchestration at operating scale
12Agents per Workflow (avg)
<500msRouting Latency
100%Audit Coverage
SHEET 08Sign-offREADY
See Multi-Agent Orchestration in Action
Watch how assistents.ai coordinates multiple agents across your real workflows, from vendor intake to compliance to payment orchestration.
- Topic
- Multi-agent orchestration
- Patterns
- 4 covered · sequential to mesh
- Controls
- Routing · conflicts · permissions · audit
- Sheet
- 08 of 08 · Guide