Multi-Agent Systems Development & Agent Orchestration

A single agent plans and acts on its own. A multi-agent system splits the work across several agents with defined roles, so one can retrieve, another can reason, and a third can verify. You choose it when a task has distinct sub-skills or needs independent checking that one agent cannot provide reliably.

When one agent with a few tools already meets the accuracy and latency target. Extra agents add message overhead, more failure points, and higher token cost. If a task is linear and does not need parallel roles or cross-checking, a single agent is cheaper and easier to debug. We say so before you build.

LangGraph for deterministic state-machine control, CrewAI for role-based teams, and AutoGen for conversational agent groups. We select by the coordination pattern the problem needs, not by preference. For tool access across agents we standardize on the Model Context Protocol so tools are reusable and auditable.

We model the system as a finite state graph with explicit termination conditions, step budgets, and a supervisor that can halt the run. Every agent transition is logged. Loops are caught by cycle limits and by a validator agent that decides when the objective is met, rather than letting agents talk indefinitely.

State is held in a single typed store that every agent reads and writes through a schema, not through free-form chat history. This prevents drift and makes each step reproducible. For long tasks we persist state so a run can pause, wait for a human approval, and resume without losing context.

Yes. We place human-in-the-loop gates at defined transitions, so an agent pauses and requests approval before a high-impact action. The run persists its state, waits, and continues once a person approves or edits the proposed step. This is standard for regulated workflows in banking and healthcare.

We evaluate task success rate, step count, cost per completed task, and the rate of failed or repeated steps, measured on a fixed benchmark set before and after changes. A system that raises success rate but triples cost is not an improvement, so we report both together rather than a single accuracy number.

Every agent call is a model call, so a five-agent system can cost five times a single agent per task. We route simple sub-steps to smaller models, cache repeated retrievals, cap step budgets, and collapse agents that do not earn their keep. Cost per completed task is tracked as a first-class metric.

Often. If you have one working agent, we wrap it as a node in a larger graph and add the coordinating agents around it. You keep what works and gain role separation, verification, and orchestration without a rebuild. We start with an assessment of the current agent before adding anything.