Introduction

Designing a reliable agent orchestration UI is about making multi‑agent systems legible, controllable, and safe for builders and operators. This checklist distills what product managers, designers, and engineers should ship to plan, execute, observe, and improve agentic workflows in production.

Who this checklist is for

Founders and PMs operationalizing AI agents beyond a prototype
Product/UX teams building control planes and run consoles
Engineers instrumenting traces, evaluations, safety, and cost controls

Core principles for agentic UX

Transparency: Show what each agent plans to do, is doing, and did, with inputs, tools, and outputs visible at every step.
Control: Allow people to pause, resume, step, retry, and override with clear, reversible actions.
Auditability: Persist runs, traces, prompts, tool IO, versions, and decisions for later inspection.
Safety: Enforce guardrails, red‑team tests, rate limits, and data handling policies by default.
Performance & cost: Stream progress, surface latencies, token usage, cash costs, and cache hits to enable tradeoffs.
Reproducibility: Version prompts, tools, and policies; pin model/tool versions; capture seeds and configs.

Essential UI surfaces (ship these first)

Surface	Purpose	Must‑have elements
Task composer	Define a task/run	Task schema, input validation, expected outputs, policy/profile selection, estimated cost/time
Run queue & live runs	Operate at scale	Status, owner, priority, SLA, pause/cancel/retry, batch actions, filters
Run detail	Investigate a run	Timeline, agent graph, per‑step logs, artifacts, diffs vs prior runs, controls (step/retry/edit input)
Graph/trace view	Understand behavior	DAG/state diagram, tool calls, model invocations, latencies, error hotspots, breadcrumbs
Agents registry	Manage agents	Name, role, capabilities, dependencies, version, change log, deprecation state
Tools registry	Manage tools	Contracts, auth scopes, rate limits, quotas, sandbox info, test harness
Prompts & templates	Version prompts	Editor, variables, tests, approvals, rollback, A/B slots
Guardrails & policies	Enforce rules	Input/output filters, allow/deny lists, PII handling, jailbreak checks, approval flows
Datasets & memory	Control context	Data sources, embeddings indexes, retention, TTLs, purge/export, provenance
Evaluations	Measure quality	Eval suites, test sets, rubrics, run comparators, score histories
System health	Keep it up	Model/tool status, incidents, rate‑limit posture, quotas, dependency health
Access & audit	Stay compliant	Roles, scopes, SSO, API keys, audit log with export

Pre‑run checklist (design and UX requirements)

[ ] Task form supports schema validation, required fields, safe defaults, and preview of derived params.
[ ] Policy/profile picker (environment, model family, temperature, safety level, max cost/time per run).
[ ] Estimated cost/time with confidence band and how it was computed.
[ ] Dry‑run/sandbox mode with synthetic data or redacted inputs.
[ ] Clear data handling notes: what will be logged, retained, or sent to third parties.

In‑run checklist (operate with confidence)

[ ] Real‑time status with streaming output and step progress indicators.
[ ] Controls: pause, resume, step, skip, retry step, cancel; each with confirmations and consequences.
[ ] Live token/latency/cost counters; color‑coded SLA posture (on track/at risk/missed).
[ ] Backoff and retry policies surfaced (rules, limits, jitter) and editable where safe.
[ ] Fallback paths displayed when primary tools/models degrade or hit rate limits.

Post‑run checklist (understand and improve)

[ ] Complete, immutable timeline with inputs, prompts, tool IO, artifacts, and decisions.
[ ] Diff vs. baseline run; highlight changed prompts, tools, and policies.
[ ] Summarized cost (tokens, API calls, cash) and performance (latency, success criteria).
[ ] One‑click bug report with auto‑attached trace and environment snapshot.
[ ] Regenerate with edits (prompt/input/policy) into a new run, linked back for comparison.

Guardrails, policy, and security

[ ] Central policy store with versioning and approval workflow.
[ ] Output filters (PII, toxicity, secrets) with quarantine and human review queues.
[ ] Allow/deny lists for domains, file types, and tool methods; explain denials in UI.
[ ] Secrets management: never echo secrets; scoped tokens; rotation reminders; break‑glass flows logged.
[ ] Data retention controls (TTL, purge/export) per dataset, with provenance and consent tracking.

Observability and evaluations

[ ] Unified tracing across agents, tools, models, and external systems; correlation IDs everywhere.
[ ] Built‑in eval harness: golden sets, rubrics (auto + human), pass/fail gates for releases.
[ ] Run comparison matrix (model A vs. B, prompt v1 vs. v2, policy X vs. Y) with statistical summaries.
[ ] Error taxonomy with auto‑triage (tool unavailability, parsing, safety, timeouts, budget exceeded).
[ ] Export traces/evals (CSV/JSON) and signed links for external review.

Performance and cost controls

[ ] Budgets per user/team/project with alerts and hard/soft limits.
[ ] Caching knobs and hit/miss visibility (prompt, tool results, retrieval indices).
[ ] Batch execution and concurrency limits with safe defaults.
[ ] Tokenization preview and truncation strategies for long contexts.
[ ] Model/tool routing policies (primary, shadow, canary) explained in UI.

Memory, data, and retrieval

[ ] Clear mental model for short‑term vs. long‑term memory and when each is used.
[ ] Source lists for retrieval: index names, last refresh, chunking, embeddings model, filters.
[ ] Per‑source toggles during a run (include/exclude) with rationale.
[ ] Hallucination reduction aids: citation slots, grounding indicators, and confidence hints.

Access, roles, and collaboration

[ ] Roles/scopes mapped to every control (e.g., who can edit prompts vs. run ops?).
[ ] Commenting on steps/artifacts; mention users; resolve threads; link to tickets.
[ ] Shareable, permissioned run views for stakeholders and incident channels.

Rollout milestones (pragmatic sequencing)

Milestone 1 (Weeks 1–3): Task composer, run detail, live streaming, pause/cancel, immutable trace, basic cost.
Milestone 2 (Weeks 4–6): Agents/tools registries, prompt versioning, eval harness, role‑based access.
Milestone 3 (Weeks 7–9): Guardrails/policies, budgets, caching, canary routing, incident views.
Milestone 4 (Weeks 10–12): Memory/retrieval controls, comparison matrix, red‑team suites, audit exports.

Success metrics to instrument

Task success rate and time‑to‑complete by task type
Mean/95p end‑to‑end latency and per‑step latency
Cost per successful task (tokens, cash) and per‑user budget adherence
Intervention rate (human‑in‑the‑loop) and rework rate
Regression rate after changes to prompts/policies/tools

Common pitfalls (and how to avoid them)

Hidden magic: Always link results to steps, inputs, and policies.
Over‑automation: Provide pause/step/override and make them discoverable.
Version drift: Pin and display versions for prompts, models, tools, and guardrails.
Unbounded costs: Ship budgets/alerts before enabling batch or external triggers.
Silent failures: Default to visible errors with actionable next steps; never swallow exceptions.

How Zypsy can help

Zypsy designs and ships brand, product, and engineering systems end‑to‑end for AI‑forward companies. See our capabilities and relevant case work:

Full‑stack product and engineering: Capabilities
Complex AI product UX at scale: Captions case study
AI security and governance UX: Robust Intelligence case study
Enterprise‑grade developer UX: Solo.io case study

FAQ

What is an agent orchestration UI? A control plane and run console that lets humans define tasks, supervise multi‑agent workflows, and inspect traces, policies, costs, and outcomes.
How is this different from a prompt playground? Playgrounds optimize for single prompts; orchestration UIs optimize for multi‑step, multi‑tool, versioned systems with observability and governance.
Do I need all surfaces on day one? No. Ship the task composer, run detail, and immutable traces first, then add policies, evals, and budgets.
Where should guardrails live? Centralized, versioned policies with approvals; enforce at input and output stages and log all decisions.
How do I add human‑in‑the‑loop later? Introduce review queues at policy violations or low confidence, with SLAs, templated feedback, and audit trails.

Download the checklist (PDF)

Need an offline copy? Request the PDF via our contact form: Contact Zypsy.