An AI agent handles a single, defined task autonomously. A multi-agent system coordinates several specialized agents working on different parts of a workflow – passing context between them, handling each other's failures, and escalating to humans at defined checkpoints. The architecture you need depends on the complexity of your workflow, not on how ambitious you want to sound.

The clearest signal is a multi-step workflow where each step depends on the output of the previous one – and where a human is currently doing the coordination in between. If you can describe the workflow as a sequence of decisions and actions, it's worth prototyping. The AI Agent Prototype package is designed specifically to answer this question on your actual data in one week.

Agent Communication Protocol is a standard for how agents in a pipeline exchange information, signal completion, and handle failures. Without it, multi-agent coordination relies on custom handoffs that break unpredictably under real load. We build ACP integration into every multi-agent pipeline – it's what makes the system reliable enough to leave running without constant supervision.

Cost in a multi-agent pipeline compounds with every agent call – an inefficient prompt or an unexpected retry loop multiplies across the whole pipeline. We design cost architecture as part of the build: prompt structure, call frequency, caching strategy, and fallback paths that don't trigger unnecessary model calls. Cost predictability is an architecture decision, not a monitoring task.

It means defined points in the workflow where an agent pauses, surfaces its output or decision to a human, and waits for approval before proceeding. The location and frequency of these checkpoints depends on the risk profile of each decision – a billing action has different requirements than a documentation draft. We design these checkpoints into the architecture before writing the first line of code.

The AI Agent Prototype takes one week. A single agent build runs 4–6 weeks. A multi-agent pipeline runs 8–12 weeks. These timelines assume a defined use case, access to your data and systems, and a stakeholder who can make decisions during the engagement. The most common cause of delays isn't technical – it's waiting for internal approvals on scope changes.

In most cases, no. Agents are designed to integrate with existing data pipelines, APIs, and infrastructure – not replace them. The architecture recommendation from the AI Agent Prototype includes a clear assessment of what needs to change, what can stay, and what the integration points look like with your current stack.