# Multi-Agent Systems Need More Than a Prompt > Source: > Published: 2026-07-14 20:34:42+00:00 Most multi-agent demos look simple. An agent receives a request, delegates a few tasks, calls some tools, and returns an answer. Then you try to use the same system for real work. Suddenly, the important questions are not about the prompt: If the answers are scattered across chat sessions, terminal logs, and database records, the system becomes difficult to trust. This is the problem we are working on with [Heym](https://heym.run). In a traditional Kanban board, cards describe work that people need to complete. In Heym's Agentic Kanban Board, a card is the work. Each column can contain an ordered chain of AI workflows. Moving a card into a column starts that chain automatically. Moving a card from Backlog to Planning is no longer just a status change. It is an execution event. The Planning column might: The Development column can then receive the same card, including the plan and the human's answers, and start a different workflow chain. The card becomes the handoff between agents, workflows, models, and people. One of the hardest parts of building multi-agent systems is preserving context across steps. A Heym card carries: Every new workflow receives the accumulated context. This means you do not need to copy the same information into another prompt or invent a custom protocol for every agent handoff. The card acts as shared memory for the process. Different columns can use different models, tools, and workflows while operating on the same persistent job. A production agent system is easier to understand when it is composed of small workflows with clear responsibilities. For example, a feature development board could use: Each workflow can be opened and inspected on Heym's visual canvas. The control flow is explicit. You can see which workflow runs in each column, which tools are available, where approvals happen, and how outputs move into the next step. This is what we mean by visible decisions. It is not hidden model reasoning. It is visible workflow routing, tool activity, retrieval results, approvals, and execution state. When a card enters a column, the workflows attached to that column run sequentially. Each successful workflow appends its output to the card before the next workflow starts. The card communicates its current state visually: This makes the board useful for both technical and non-technical team members. A developer can inspect the execution details, while someone from product or operations can understand whether a job is running, blocked, completed, or failed. Full autonomy is not always the goal. Some decisions need context that only a person can provide. Others need explicit approval because they affect customers, production systems, or external services. Heym workflows can pause for: The execution state is persisted while the card waits. Once someone answers or approves the request, the workflow resumes from the same point. The rest of the column's workflow chain continues automatically. This is especially useful during planning. A planning workflow can enrich a request and ask questions. The card stays in Planning until someone responds in the comment thread. That response becomes part of the card's context and releases it to the next stage. If the plan needs more work, you can run another follow-up round without losing the previous discussion. When an agent produces the wrong result, the final response rarely tells you enough. You need to inspect the execution. Board runs in Heym are connected to the same execution history and tracing system used by other workflows. You can inspect: This makes it possible to understand where the system failed without replaying the entire process from memory. The board shows the operational state. The execution traces show the technical details behind it. A useful first workflow looks like this: This is a small loop, but it contains the important parts of a production agent system: You can expand the system by adding new workflows to the chain instead of turning one prompt into an increasingly complicated block of instructions. Prompts are part of an agent system, but they are not the system. A reliable multi-agent workflow also needs memory, control flow, approvals, execution state, and observability. That is what we want the Agentic Kanban Board to provide: a shared surface where people can see what agents are doing, understand what happened, and step in when needed. You can read the complete technical walkthrough and import the starter planning workflow here: [Agentic Kanban Board: Loop Engineering for AI Agents](https://heym.run/blog/agentic-kanban-board) Heym is source-available and self-hostable. You can also explore the repository on [GitHub](https://github.com/heymrun/heym). Build, run, and debug multi-agent systems with visible decisions, memory, approvals, and execution traces.