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A useful live village dashboard is an event-driven operations interface, not a page that repeatedly polls agent status. Give every conversation a durable thread_id, every execution a run_id, and every nested worker a namespace path. Stream typed lifecycle, message, tool, state, update, checkpoint, and human-input events over SSE or WebSocket; initialize the browser with a complete state snapshot, then apply ordered deltas and replay missed events after reconnects. Render agents as nodes, real handoffs as edges, and the event timeline as the audit record. Add OpenTelemetry for model generations, tools, latency, tokens, cost, errors, and quality so the view helps you operate the system rather than merely watch an animation.
What is a live village dashboard for AI agents?
A village dashboard is a visual control room for a multi-agent workflow. Each agent appears as a resident or node, nested workers appear inside namespace paths, handoffs become edges, and a chronological event stream explains what happened. The map gives you a fast answer to “who is working on what?” while the timeline preserves the exact sequence needed to diagnose failures, approvals, retries, and state changes.
Polling an agent registry every few seconds is not enough. Polling can miss short-lived tool calls, reorder updates that arrive at different times, and leave the browser unable to distinguish “nothing happened” from “the server dropped an event.” A typed stream carries lifecycle boundaries, sequence numbers, state snapshots, and replay metadata so the visual layer can be rebuilt without guessing.
How should I model threads, runs, and nested agents?
Start with durable identities before designing the map. A thread identifies the conversation or workflow, a run identifies one execution of that thread, and a namespace identifies an agent and its descendants. Agent Protocol treats threads, runs, and long-term storage as production primitives; using those identities in the UI makes reconnects and historical inspection stable.
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| Object | Purpose | Useful fields | Dashboard behavior |
|---|---|---|---|
| Thread | Durable routing key for a conversation or workflow | thread_id, title, created time, retention state |
Opens the correct stream and groups all runs |
| Run | One execution attempt within a thread | run_id, start and end times, status, parent run |
Filters the timeline and separates retries or branches |
| Namespace | Path for an agent and its nested children | Path segments, parent namespace, depth | Controls graph nesting and subscriptions |
| Agent registry entry | Current operational summary for a node | Display name, role, status, current task, parent namespace, last event sequence | Drives badges, labels, and stale-node warnings |
Keep the registry as a projection of events, not as a second source of truth. When an agent starts, finishes, fails, or pauses, the corresponding event updates its registry entry. The event timeline remains authoritative; the registry is the fast index used to draw the village.
What should the canonical event envelope contain?
Use one envelope for every channel so the browser can order, filter, store, and replay events consistently. The minimum useful fields are an event identifier, a monotonic sequence, thread and run identities, a namespace, a timestamp, a method name, and typed data.
{"eventId":"evt_01J...","seq":1842,"threadId":"thread_7f","runId":"run_22","namespace":["planner","researcher"],"timestamp":"2026-10-26T12:04:31.482Z","method":"tools.tool-output-delta","data":{"toolCallId":"call_91","delta":"..."}}eventId is the transport cursor used for reconnection. seq is a monotonic ordering key within the stream scope; persist the last applied value for each thread and namespace. Keep timestamps for display and latency calculations, but do not use wall-clock time to order events when sequence numbers are available.
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- Lifecycle: started, running, completed, failed, and interrupted states.
- Messages: complete messages and content-block deltas for streaming model output.
- Tools: tool-started, tool-output-delta, tool-finished, and tool-error events.
- Values: complete state snapshots that can establish a client baseline.
- Updates: node or step deltas that change part of the current state.
- Checkpoints: branch, fork, and time-travel metadata.
- Input: human-in-the-loop requests and correlated responses.
- Custom: application-specific progress events that do not fit the standard families.
Keep message chunks and tool-output chunks typed rather than embedding arbitrary log text. The browser can then place a token delta in a message panel, a tool delta in a call inspector, and a lifecycle event on both the node and the timeline without parsing prose.
Should I use SSE or WebSocket for the dashboard?
Choose SSE when the browser mainly receives server-pushed events and sends commands through ordinary HTTP. Choose WebSocket when the interface needs a full-duplex session with subscription changes, reconnect commands, and control messages on the same connection.
| Transport | Documented shape | Best fit | Trade-off |
|---|---|---|---|
| SSE | POST /threads/:thread_id/stream with a request selecting channels, namespaces, depth, and replay position |
Read-heavy dashboards, simple browser delivery, HTTP-based commands | Commands travel through separate HTTP requests |
| WebSocket | Upgrade GET /threads/:thread_id/stream; send subscription and reconnect commands on the connection |
Interactive control rooms with frequent subscription or input changes | Requires connection lifecycle handling in both client and server |
Both choices can support the same event model. Treat transport as a delivery layer; do not create one schema for SSE and another for WebSocket. A browser should be able to switch transports without changing how it applies snapshots, deltas, or checkpoints.
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How do I build reconnect and replay correctly?
Make resynchronization a first-class feature before polishing the map. The Agent Streaming Protocol defines sequence-based replay, event IDs for transport reconnection, and explicit state commands for rebuilding a client.
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- Subscribe with a baseline. On the first connection, request the
valueschannel so the server sends a complete state snapshot for the selected thread and namespace. Render that snapshot before applying incremental events. - Persist cursors. After each successfully applied event, store the latest
eventIdandseqper thread and namespace. Persist them outside transient component state so a browser refresh does not reset the cursor. - Reconnect with the cursor. For SSE, provide the documented
sincereplay position. For WebSocket, sendsubscription.reconnectwith the last event ID or sequence. - Apply only newer events. Discard a duplicate event whose sequence is already committed, and pause dependent UI updates when a sequence gap appears.
- Recover from retention gaps. If the server no longer retains the requested event, issue the state command, accept a new full snapshot, reset the cursor to that snapshot’s sequence, and redraw dependent projections.
- Record the recovery. Put a visible resynchronization marker in the run timeline. Operators should know that the browser rebuilt its view instead of assuming the gap never occurred.
This pattern prevents the most damaging dashboard failure: a map that looks current while silently missing a tool error or approval request. The snapshot is the baseline; updates are an optimization, not a replacement for state.
How do I render agents, handoffs, and tool calls as a village?
Use two coordinated panes: a graph for orientation and a timeline for proof. The graph answers where work is happening; the timeline answers what happened, in which order, and under which run.
Design the node cards
- Show the agent’s display name and role from the registry.
- Use lifecycle badges for running, completed, failed, interrupted, or waiting states.
- Display the current task and the timestamp of the last event.
- Show compact counters for active tool calls, message activity, token usage, or cost when telemetry is available.
- Mark a node as stale when its last sequence has not advanced, while keeping the last known state visible.
Draw only meaningful edges
Create an edge when an event records an actual send, tool call, graph transition, or child-agent spawn. Do not animate a connection merely because two agents are adjacent in the layout. Store the source namespace, destination namespace, event ID, and run ID on each edge so selecting it opens the exact timeline entry.
Keep the timeline authoritative
The selected namespace should expose message deltas, tool inputs and outputs, errors, latency, token use, checkpoints, and human-input events. Group adjacent content-block deltas into one expandable message while preserving their sequence metadata. A compact overview can aggregate activity, but drill-down must retain the original event boundaries.
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How can I visualize nested agents and explain who spawned whom?
Represent a namespace as an ordered path rather than a flat agent name. For example, [planner, researcher, browser] tells the operator that browser belongs to researcher, which belongs to planner. Subscribe by namespace and depth so the overview can show top-level residents while a selected node reveals descendants.
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Lifecycle causes make the graph explainable. Attach the cause when a child starts: a parent tool call, a send operation, or a graph edge. When the child finishes or fails, keep the parent event reference on the timeline. This lets an operator answer whether a branch was intentionally spawned, triggered by a tool, or created by a retry.
Use depth limits deliberately. A shallow subscription keeps a busy village readable and reduces event volume; a deeper subscription is appropriate while investigating a specific branch. Because the namespace is part of every envelope, changing the visible depth does not change run identity or replay semantics.
How do I add human approval and other controls?
Human intervention should appear as explicit state, not as a hidden button beside the graph. When an agent pauses, emit an input.requested event containing the interrupt ID, requested action, and available response choices.
- Render the request as a card attached to the waiting agent and the exact namespace that issued it.
- Show the interrupt ID, the action requiring approval, relevant tool arguments, and the choices accepted by the server.
- Disable duplicate submissions after the operator chooses an option, but keep the card visible while the response is in flight.
- Submit the correlated
input.respondmessage and include any permitted state update or graph jump. - Append both request and response to the run timeline, then wait for the resulting lifecycle or state event before marking the agent resumed.
Correlation matters when several agents pause at once. The interrupt ID, thread, run, and namespace together prevent an approval intended for one branch from being applied to another.
What should I collect with OpenTelemetry?
Streaming tells you what agents are doing; telemetry tells you whether the system is affordable, fast, reliable, and useful. Use OpenTelemetry as the runtime spine and connect spans to the same thread, run, and namespace identifiers used by the dashboard.
| Signal | Capture | Operational question it answers |
|---|---|---|
| Generation | Model provider, model name, input messages, output messages, request timing | Which model call is slow or producing unexpected output? |
| Tool execution | Tool name, arguments, result metadata, start and finish times | Is a tool causing the branch to stall or fail? |
| Usage | Input and output tokens, cost metadata | Which thread, run, or agent is consuming budget? |
| Errors | Exception type, message, retry or interruption state | Where did the run diverge from its expected path? |
| Quality | Application-defined quality scores and evaluation links | Did a technically successful run satisfy the task? |
Grafana’s agent-observability documentation describes generation-level capture, conversation views, traces, token and cost breakdowns, quality scores, and framework integrations. AutoGen documents OpenTelemetry tracing and compatibility with OpenTelemetry backends such as Jaeger. For AWS-hosted deployments, AWS documents exporting traces from LangGraph, LangChain, Strands Agents, CrewAI, OpenAI Agents, and the Vercel AI SDK running on AgentCore, Lambda, EC2, ECS, or EKS to CloudWatch.
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Keep high-cardinality message content out of labels. Put sensitive prompts, tool arguments, and outputs in controlled span attributes or linked records subject to redaction, while using low-cardinality dimensions such as provider, model, namespace, status, and environment for dashboards.
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How should I separate live state from durable history?
Use two storage paths. An in-memory or low-latency event buffer serves active dashboards; durable run history supports replay, audit, checkpoint inspection, and analytics after the live window has passed.
Hot path
Keep recent events, active subscriptions, the latest state snapshot, and cursors in the low-latency layer. Its job is to deliver a responsive village and satisfy short reconnects.
Durable path
Persist the canonical event record, run metadata, checkpoints, and selected snapshots for the retention period your operations and compliance requirements demand. Fetch large message bodies and detailed traces on demand instead of placing every payload in the frequently refreshed village state.
Snapshot policy
Snapshot the state needed to redraw the overview frequently enough that a reconnect does not require replaying an unbounded event history. Keep detailed messages, tool payloads, and traces separate when the graph only needs status, current task, and last sequence.
How do I compare implementation stacks?
Compare the complete operating model, not just the streaming protocol. A stack that delivers events quickly but cannot replay missed data or expose checkpoints will produce an attractive but untrustworthy dashboard.
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| Axis | Questions to ask | Why it changes the design |
|---|---|---|
| Transport | Does it support SSE, WebSocket, or both? | Determines browser connection and command patterns |
| Ordering and replay | Are event IDs, sequence numbers, and missed-event recovery defined? | Determines whether reconnects preserve correctness |
| Nested execution | Can subscriptions filter by namespace and depth? | Controls graph readability and event volume |
| State model | Are snapshots, deltas, checkpoints, and forks available? | Determines how the client rebuilds and inspects history |
| Human control | Are interrupt and response messages first-class? | Prevents approvals from becoming untracked side effects |
| Observability | Can it capture generations, tools, latency, tokens, cost, and quality? | Turns the view into an operations system |
| Operations | What are retention, access control, redaction, and failure-recovery behaviors? | Sets the security and support burden |
| Cost | How do ingestion, storage, evaluation, and retained history scale? | Prevents a detailed event stream from becoming an uncontrolled bill |
The Agent Protocol documentation supplies the transport, replay, namespace, state, and lifecycle dimensions. Grafana, AutoGen, and AWS documentation cover telemetry and evaluation integrations. Those documents describe capabilities rather than independent performance benchmarks, so size your own system with representative load tests instead of quoting a universal throughput number.
What is a practical build order?
Implement correctness in layers so the visual map never outruns the underlying record.
- Define identities and authorization boundaries. Establish thread, run, namespace, and event ownership rules before exposing any stream endpoint.
- Publish the envelope. Version the method names and data shapes, require monotonic sequence assignment, and document which fields are safe to persist.
- Implement one replayable stream. Deliver lifecycle, values, updates, and tool events first; verify that a client can rebuild state from a snapshot plus ordered deltas.
- Add transport adapters. Expose the documented SSE path, then add WebSocket support if the control room needs full duplex subscriptions or commands.
- Build the timeline before the map. Confirm ordering, duplicate handling, gap recovery, and checkpoint links with a plain event list.
- Add the village projection. Derive node status, current task, parent-child edges, and stale indicators from the same events shown in the timeline.
- Add namespace filtering. Let operators move from a top-level overview to a selected descendant without creating a second identity model.
- Add input controls. Implement request and response correlation, disabled duplicate actions, and visible before-and-after state.
- Attach OpenTelemetry. Correlate spans with thread, run, and namespace IDs, then expose latency, usage, cost, errors, and quality links.
- Test failure paths. Exercise dropped connections, duplicate delivery, out-of-order arrival, expired replay windows, child-agent failure, and interrupted runs before calling the dashboard production-ready.
What does production readiness look like?
- A refresh or reconnect can recover the same thread and run without inventing a new identity.
- The browser starts from a full snapshot and never applies an update whose prerequisite sequence is missing.
- Every visible edge points to a real send, tool call, transition, or child-agent cause.
- Nested agents can be filtered by namespace and depth without losing their parent relationship.
- Human approvals have correlated request and response records.
- Operators can move from a node to its messages, tools, traces, checkpoints, token use, cost, and errors.
- Hot buffering, durable retention, redaction, and access control have explicit policies.
- Load tests and failure drills reflect your event rates and payload sizes; no generic benchmark is treated as a guarantee.
Frequently Asked Questions
Can one browser monitor several agent threads at once?
Yes, but keep a separate cursor and subscription state for each thread_id. A combined operations screen should merge events only for display; replay, authorization, and snapshot recovery must remain thread-scoped so one busy workflow cannot advance another workflow’s sequence.
How should custom progress events appear in the stream?
Put application-specific milestones in the protocol’s custom family while retaining the normal envelope, sequence, namespace, and run fields. The UI can render them as progress markers without teaching the transport a new ordering or reconnect rule.
Should every streamed token be drawn on the village graph?
No. Route content-block deltas to the selected agent’s message panel and use aggregate counters or a subtle activity badge on the node. This keeps graph rendering stable while preserving the complete token stream in the timeline and durable record.
What should an operator see when a child agent runs for several minutes?
Keep the parent edge active, show the child’s lifecycle state and elapsed time from its start event, and surface tool or custom progress events in the side panel. If no new sequence arrives, distinguish an expected long task from a stalled connection by showing the last event time and stream health separately.
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