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.
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.
The difference between individual agents and coordinated systems.
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.
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.
Active coordination of multiple agents. The orchestrator routes tasks, manages dependencies, resolves conflicts, enforces permissions, and reports outcomes. Coordination is explicit and governed.
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.
Four proven patterns for coordinating multiple agents.
Agent outputs feed directly into the next agent in sequence. Ideal for linear workflows with defined handoffs.
Invoice extraction → validation → approval → payment processing
Multiple agents process the same input simultaneously. Results are merged and consolidated by a coordinator.
5 agents analyze credit risk, market exposure, operational risk, regulatory compliance, and financial health in parallel
A manager agent evaluates tasks and delegates to specialist agents. Synthesizes results into final decisions.
Executive agent routes customer issues to sales, support, or retention agents based on context
Agents react independently to events in a shared event stream. Coordination happens through state and rules.
When a contract is signed, notifications trigger billing, onboarding, and compliance agents autonomously
Three critical failure modes and how to prevent them.
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.
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.
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.
Comparison with DIY approaches and legacy RPA systems.
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.
Specialist agent
Specialist agent
Specialist agent
Specialist agent
Coordination layer
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.
Watch how assistents.ai coordinates multiple agents across your real workflows, from vendor intake to compliance to payment orchestration.