Agent Orchestration & Prompt Management UI

Introduction

This page provides a reference specification and delivery blueprint for an Agent Orchestration & Prompt Management UI that centralizes prompt assets, governs changes, and safely ships updates to production LLM-powered experiences. It is written for founders, product, platform, ML, and security teams who need versioning with human‑readable diffs, staged rollout/rollback, and complete auditability—delivered as a production‑ready web app and API. Zypsy designs and engineers systems like this end‑to‑end, pairing brand, product, and full‑stack execution in sprint formats or via services‑for‑equity through Design Capital and cash engagements (see Capabilities).

Agent Orchestrator UI — overview

This blueprint doubles as an Agent Orchestrator UI for teams standardizing Agent Orchestration and a production‑grade Prompt Management UI. It unifies prompt assets and multi‑agent graphs into a single control plane with versioning, human‑readable diffs, staged rollout/rollback, and full auditability.

Tools & ecosystems (examples)

Designed to fit your stack, not replace it. The UI and API integrate with popular agent and eval ecosystems, including LangGraph, CrewAI, OpenAI Assistants API, LangSmith, and Azure AI Studio—alongside existing CI/CD, observability, and data tooling.

Agent Orchestrator UI (control plane) — checklist

• Central prompt/agent inventory with typed variables and ownership

• Semantic versioning and human‑readable text/JSON diffs

• Staged rollout/canary by percent or segment; one‑click rollback

• Immutable audit trail, RBAC, SSO/SAML, SCIM

• Evaluations and quality gates (golden sets, auto‑evals, HITL)

• Observability and cost guardrails (latency, error, $ budgets)

• Runtime SDK/CLI and Git/CI sync for promotions and rollback

• Secrets management, PII controls, residency and policy‑as‑code

Agent Control Plane UI: Versioning, Diffs, Rollout/Rollback & Audit

For searchers and teams evaluating “agent control planes,” this UI is explicitly designed as the control plane for LLM prompts and multi‑agent graphs—centralizing assets, enforcing governance, and safely shipping changes with staged rollout and instant rollback.

Control‑plane checklist (copy‑paste):

• Versioning by semantic release with lineage and approvals

• Human‑readable text/JSON diffs with incompatible‑change detection

• Staged rollout/canary by percent or segment, with safe deployment gates

• One‑click rollback and global/per‑asset kill switches

• Immutable audit trail with RBAC, SSO/SAML, and SCIM provisioning

• Observability and cost guardrails: latency/cost ceilings, incident links, budgets

One‑screen spec (conceptual, alt="Agent control plane UI"):

[Left nav]
  • Inventory (Prompts, Agents, Tools)
  • Releases (Envs: Dev/Staging/Canary/Prod)
  • Diffs & Approvals
  • Evaluations
  • Incidents & Audit
  • Policies & RBAC

[Main — Release: agent:planner v2.4.0]
  Header: Status: Canary 10% | Gates: Eval ✓  Latency ⚠  Cost ✓ | Rollback → v2.3.6 (LKG)
  Tabs: Overview | Diffs | Eval Runs | Traffic | Audit

  Overview
    • Traffic allocation: 10% us‑east, tierA
    • Gates: eval ≥95%, p95 < 1.8s, cost < $0.015, err < 2%
    • Controls: Increase %, Hold, Kill Switch, Rollback

  Diffs (side‑by‑side)
    • Text: token/sentence diffs, variable changes
    • JSON/Graph: AST‑aware, incompatible‑change check

  Audit (immutable)
    • Created, approved, promoted, secret read, policy update

Product objectives and scope

• Unify all prompts, agent graphs, and tool definitions in one UI with APIs/SDKs for runtime retrieval.

• Ship changes safely with versioning, side‑by‑side diffs, approvals, staged rollout, and one‑click rollback.

• Provide full observability: cost, latency, quality metrics, incident traces, and model usage.

• Enforce governance: RBAC, audit logs, data controls, and separation of duties.

• Fit into existing CI/CD and analytics; no vendor lock‑in to a specific LLM/provider.

• Non‑goals: training/fine‑tuning infrastructure; data labeling platform; model hosting.

Core specifications

Prompt inventory and schema

• Object types: System Prompt, User Prompt Template, Tool/Function Schema, Agent Policy, Retrieval Template, Guardrail Policy.

• Metadata: owner, team, tags, description, variables, default model/profile, sampling params, safety settings, cache hints, cost budget, SLAs.

• Variables: typed with validation, defaults, PII flags, and evaluation scaffolds (golden cases per variable set).

• Lifecycles: draft → review → approved → released; environment scoped (dev, staging, canary, prod).

Versioning and diffs (human‑legible + structured)

• Semantic versions (major.minor.patch) with change notes and required approvers (code‑owners).

• Text diffs: side‑by‑side with token and sentence diff views; highlight variable insertions/removals; reading‑level delta; prompt length/tokenization preview.

• JSON/Schema diffs: AST‑aware diff for tool schemas and agent graphs; incompatible‑change detection gates.

• Lineage: show parent/child branches; cherry‑pick and squash; link commits to experiment IDs and incidents.

• Rollback: instant revert to any prior, with automatic cache‑bust and dependency integrity checks.

Rollout and rollback

• Environments: dev, staging, canary, production (configurable); promotion requires passing gates.

• Traffic allocation: percentage‑based, segment‑based (tenant, geography, cohort, entitlement), or header flag.

• Safe deployment gates: eval pass rates, regression thresholds, latency/cost ceilings, incident budget.

• Kill switch: global or per‑prompt/agent; warm rollback with cached last‑known‑good.

• Release automation: schedule windows, freeze periods, and approvals; Slack/email notifications.

Audit logs and governance

• Immutable audit stream for: create/change/delete, approvals, releases, rollbacks, secret reads, policy updates, data exports.

• Tamper‑evident hashing with chained entries; export to SIEM; retention policies per environment.

• RBAC: roles (Viewer, Editor, Approver, Release Manager, Admin), resource‑scoped permissions, temporary access (time‑boxed elevation), SSO/SAML + SCIM.

• Policy center: prompt length caps, PII redaction rules, data residency controls, model/provider allow‑lists.

Evaluation and quality gates

• Golden sets: task‑specific exemplars and expected outputs (text and structured JSON), with graded rubrics.

• Automatic evals: exact match, semantic similarity, function‑call correctness, safety/red‑team checks, hallucination probes, determinism under seeds.

• Human‑in‑the‑loop: review queues, rubric scoring, disagreement flags, adjudication notes.

• Experimentation: A/B/n with power analysis, sequential testing, lift/confidence dashboards.

Test bench and sandboxes

• Prompt runner: supply variables, model profile, tool mocks, and seeds; view output, token usage, and cost estimates.

• Tool simulation: deterministic tool responses and error injection; latency shaping.

• Trace explorer: per‑turn chain of thought visibility controls (if applicable), tool calls, retries, fallbacks, and guardrail actions.

Agent orchestration

• Visual graph editor for multi‑agent flows (planner, worker, reviewer) with tool nodes and memory stores.

• Context connectors: retrieval templates, vector/query configs, caching policies; dependency graph warnings.

• Resilience: timeouts, retries/backoff, circuit breakers; per‑node budgets and latency SLOs.

Observability and cost control

• Metrics: latency percentiles, token usage, provider cost, error/timeout rates, safety events, cache hit rate.

• Budgets: per‑env/model/agent caps; alerting on burn rate; auto‑degrade strategies (fallback models, shorter prompts).

• Incident management: error spikes auto‑open an incident with attached traces; one‑click rollback link.

Security and data controls

• Secrets vault for API keys and connectors; rotation workflows and access logs.

• PII controls: detection/redaction in logs, scoped retention, and residency tagging.

• Data export: scoped CSV/JSON exports with watermarking and approval.

Integrations and delivery

• Git sync: mirror prompt/graph versions to a repo; PR checks reflect evals and gates.

• CI/CD: CLI for promotions, environment diffs, and rollback; policy‑as‑code bundles.

• SDKs/Runtime: server‑side fetch of immutable prompt snapshots by version or release label; client hints for cache.

• Notification hooks: Slack/Teams, email; ticketing integrations for approvals/incidents.

Minimal data model (entities → key relations)

• Prompt/Agent → Versions → Releases → Environments.

• Evaluations → Test Cases → Rubrics → Runs (by version+env).

• Policies → Assignments (by team/resource).

• Audit Entries → Subjects (user/service) → Resources (prompt/version/env).

KPIs and SLOs

• MTTR for rollback; change failure rate; eval pass coverage; incident frequency; P50/P95 latency; $/1k requests; cache hit rate; time‑to‑approve; prompt inventory freshness.

Release gates and rollback triggers (reference)

Safe rollout checklist (copy‑paste)

- [ ] Create/update prompt or agent graph; add owner, tags, SLAs, and change notes

- [ ] Link to golden set and run automatic evals; meet threshold (e.g., ≥95% pass for critical)

- [ ] Peer review: human‑readable diff approved by required approvers (code‑owners)

- [ ] Verify AST/JSON schema diffs for tools/graphs; no incompatible changes

- [ ] Set canary plan: traffic %, target cohorts, kill switch owner, rollback label (LKG)

- [ ] Define gates: eval, latency P95 target, cost cap, error/SLO, safety incident budget

- [ ] Schedule window; notify channels (Slack/email); freeze period configured if needed

- [ ] Promote to staging; observe traces/metrics; resolve regressions

- [ ] Canary release; monitor dashboards; hold/increase % per gates

- [ ] If breach: trigger auto‑degrade or rollback; open incident with attached traces

- [ ] Post‑release review; update rubrics/golden sets; document learnings in runbook

Example: release gate config (JSON)

{
  "release_label": "v2.4.0-canary-03",
  "targets": {
    "environment": "canary",
    "traffic_percent": 10,
    "segments": ["us-east", "tenant:tierA"]
  },
  "gates": {
    "eval_pass_rate": { "threshold": 0.95, "window_requests": 1000, "action": "block_promotion" },
    "latency_p95_ms": { "threshold": 1800, "action": "hold_traffic" },
    "cost_per_request_usd": { "threshold": 0.015, "action": "auto_degrade" },
    "error_rate": { "threshold": 0.02, "action": "rollback" },
    "safety_violations_per_1k": { "threshold": 1, "action": "kill_switch" }
  },
  "degrade_strategies": [
    "fallback_model:gpt-4o-mini",
    "shorten_context:-20%",
    "reduce_tool_calls:noncritical"
  ],
  "notifications": { "slack_channel": "#llm-releases", "email": ["approvers@company.com"] },
  "owner": "release_manager@company.com",
  "rollback_label": "v2.3.6-LKG"
}

Frequently asked questions

What is an agent orchestration UI?

An agent orchestration UI is a control plane for building, versioning, testing, and safely shipping multi‑agent workflows and prompts. It centralizes assets (prompts, tools, graphs), enforces governance (RBAC, audit logs, policies), and manages staged rollout/rollback with measurable quality gates and observability.

Implementation blueprint with Zypsy

• Discovery (1–2 weeks): map flows, risks, data classes, eval requirements, and governance; define target KPIs.

• Design sprints (brand, UX, design system): information architecture, diff patterns, graph editor, observability dashboards, and approval workflows. See breadth of delivery in Capabilities.

• Build sprints (6–8 weeks typical for v1): UI, management API, SDK/CLI, Git/CI hooks, audit stream, and minimal runtime adapters; harden gates and rollback.

• Hardening & rollout (2–3 weeks): golden‑set expansion, perf tuning, RBAC/policy setup, and production canary.

• Engagement models: cash projects or services‑for‑equity via Design Capital; Zypsy can also pair cash investment via Zypsy Capital where fit.

Quick‑Start (CTA)

• Step 1 — Share context: objectives, current stack, and top workflows. Start here: Contact Zypsy.

• Step 2 — 30‑min scoping call: align goals, risks, and success metrics.

• Step 3 — 10‑day discovery sprint: IA, governance model, eval strategy, and build plan.

• Step 4 — Ship v1 in 6–8 weeks: versioning/diffs, staged rollout/rollback, and audit logs live in production.

• Optional — Apply to Design Capital: up to ~$100k of design work for ~1% equity (SAFE) over 8–10 weeks; see announcement and details in Design Capital and press coverage in TechCrunch’s overview of Zypsy’s program (context in TechCrunch).

Why Zypsy for this UI

• Integrated delivery: brand → product → web → code under one roof, reducing handoffs and shipping faster (see Capabilities).

• AI and complex‑systems track record: selected work across AI security and platforms—Robust Intelligence, Captions, Solo.io, and Copilot Travel.

• Flexible partnership: cash projects, services‑for‑equity via Design Capital, or paired with Zypsy Capital.

• Outcomes: 40+ new brands shipped; clients report $2B+ valuation gains since inception (see About).

Security, privacy, and compliance considerations

• SSO/SAML, SCIM provisioning; least‑privilege RBAC; short‑lived service tokens; IP allow‑lists.

• Encryption in transit/at rest; customer‑managed keys option; key rotation.

• PII detection and redaction in logs; configurable retention; residency tags.

• Admin guardrails: two‑person approvals for production releases; break‑glass with justification.

Operating model and ownership

• Roles: Prompt Owners (content), Approvers (domain experts), Release Managers (SRE/Platform), Admins (security), Observers (analytics/legal).

• Cadence: weekly release windows, canary first; monthly rubric refresh for evals; quarterly policy reviews.

• Documentation: runbooks for rollback, incident response, PII handling, and provider outages.

Extensibility roadmap (post‑v1)

• Policy‑as‑code packs for industry templates (e.g., stricter PII, export controls).

• Advanced experiment designs: CUPED, multi‑armed bandits with guardrails.

• Automated prompt refactoring suggestions from production traces.

• Cross‑model differential testing and cost/quality frontier analytics.

Related Zypsy work (credibility)

• AI security and enterprise governance patterns: Robust Intelligence.

• High‑scale AI UX and rapid design system delivery: Captions.

• Complex technical narrative and information architecture at scale: Solo.io.

For inquiries or to scope your v1, start here: Contact Zypsy.