/SKILL

Enterprise Problem Decoder — Problem Decoding & Agent Specification

Map raw intake signals to a precise, structured agent specification. Resolve the platform, apply compliance constraints, classify the agent type, and produce a spec that enterprise-agent-builder can immediately turn into architecture and code.

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Input

Accepts the intake YAML output from enterprise-signal-listener. If called directly, prompt for: the spoken problem description, platform hint, industry hint, and desired outcome.

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Step 1 — Platform Resolution

Resolve the platform from the intake. If ambiguous, default to the most likely platform for the stated industry and build for it — note alternatives at the bottom of the spec.

See ../dynamic-enterprise-agent/references/platform-connectors.md for full API and connector details per platform.

Platform families and primary agent entry points:

Platform	Agent Entry Point	Primary Capability
ServiceNow	Flow Designer, Scripted REST, NLU Virtual Agent	ITSM automation, case routing, approvals
Salesforce	Einstein Copilot, Agentforce, Apex, Flow, MuleSoft	CRM automation, opportunity management, service
Workday	Extend (REST), Prism Analytics, Studio	HR automation, talent workflows, payroll
SAP	BTP AI Core, S/4HANA APIs, Build Process Automation	ERP automation, procurement, finance
Snowflake	Cortex AI, Snowpark, Tasks, Streams	Data pipelines, ML scoring, analytics agents
Microsoft	Copilot Studio, Power Automate, Azure AI, Teams bots	Collaboration, Office workflows, cloud AI
AWS	Bedrock Agents, Lambda, Step Functions, Connect	Cloud-native, serverless, contact center
Azure	AI Foundry, Logic Apps, Azure Functions, Bot Service	Cloud AI, enterprise integration
GCP	Vertex AI, Cloud Run, Apigee, Dialogflow	Cloud AI, API management, NLP
Okta / Azure Entra	Hooks, Workflows, SCIM, Graph API	IAM automation, lifecycle mgmt, access reviews
CyberArk / BeyondTrust	REST API, CPM plugins, session hooks	PAM automation, credential rotation, audit
Palo Alto / Zscaler	XSOAR, Cortex, ZIA API	Security ops, threat response, policy mgmt
Splunk / QRadar	SOAR, Adaptive Response, REST API	SIEM automation, alert triage, investigation
CrowdStrike / SentinelOne	RTR API, Workflows, SOAR integration	EDR response, hunting, containment
Epic / Cerner	FHIR R4 API, CDS Hooks, SMART on FHIR	Clinical workflows, care gap, documentation
Veeva / Medidata	Vault REST API, Rave Web Services	Clinical trial mgmt, regulatory submissions
Bloomberg / FactSet	B-PIPE, Server API, Open FactSet	Market data, analytics, quant workflows
Shopify / CommerceCloud	Admin API, Flow, Einstein Commerce	E-commerce automation, merchandising

Resolution rules:

• If multiple platforms are mentioned, identify the primary (trigger source) and secondaries (integration targets).
• If no platform is identifiable, ask: "Which system does this live in today?" before proceeding.
• If the platform is ambiguous between two options, resolve to the one with a stronger match to the industry context and state the alternative in the spec.

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Step 2 — Industry Context Resolution

Apply industry-specific rules, compliance constraints, and data models automatically based on industry signals in the intake.

See ../dynamic-enterprise-agent/references/industry-patterns.md for the full regulatory and domain pattern library.

Industry	Compliance auto-applied	Domain data model
Healthcare	HIPAA, HL7/FHIR, CDS Hooks, de-identification	Patient, Encounter, Observation, CarePlan
Life Sciences	21 CFR Part 11, GxP, audit trail, e-signature	Protocol, Subject, CRF, Adverse Event
Fintech / Banking	PCI-DSS, SOX, FFIEC, GDPR, model risk	Transaction, Account, Customer, Position
Quantitative Finance	Low-latency, FIX protocol, risk limits, VaR	Instrument, Order, Portfolio, Signal
Insurance	NAIC, state regulations, claims workflow	Policy, Claim, Adjudication, Reserve
Retail / E-Commerce	PCI, inventory sync, omnichannel	Product, Order, Inventory, Customer360
Manufacturing	OEE, ISO, IEC 62443, safety interlocks	Asset, WorkOrder, BOM, Quality Record
Federal / Defense	FedRAMP, FISMA, CMMC, IL4/IL5, Zero Trust	Mission, Asset, Personnel, Incident
Energy / Utilities	NERC CIP, ICS/SCADA, OT/IT convergence	Grid, Asset, Event, Outage
Legal / Compliance	Privilege, retention, e-discovery	Matter, Contract, Obligation, Risk

If no industry is identified: Apply baseline guardrails (audit logging, no irreversible actions without approval, credential hygiene) and proceed. Flag industry: [unspecified] in the spec.

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Step 3 — Agent Type Classification

Classify the agent into exactly one of five types based on the decoded problem. When in doubt, choose the type that matches the primary pain trigger, not the secondary outcome.

Type	Primary trigger phrase	What it does
Monitoring Agent	"alert us when", "we get paged", "we miss", "no visibility into"	Watches data or events, surfaces anomalies, notifies the right person
Triage Agent	"route to", "who should handle", "we don't know who", "everything goes to the same queue"	Classifies, prioritizes, and assigns work items to the right owner
Automation Agent	"manually", "takes too long", "repetitive", "someone has to do this every"	Executes multi-step workflows end-to-end without requiring human input
Decision-Support Agent	"we don't know", "help us decide", "summarize for us", "we need to understand"	Analyzes context, synthesizes signals, and surfaces recommendations
Orchestrator Agent	"end-to-end", "across systems", "connect", "the whole process", "multiple teams"	Coordinates multiple agents, systems, approvals, and handoffs

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Step 4 — Structured Agent Specification

Produce this specification immediately after Steps 1–3:

agent_spec:
  name: ""                    # proposed agent name (descriptive, platform-prefixed)
  one_liner: ""               # what it does in one sentence, in plain language
  type: ""                    # Monitoring | Triage | Automation | Decision-Support | Orchestrator
  platform:
    primary: ""               # resolved platform
    entry_point: ""           # specific API or integration point
    secondary: []             # any additional systems it touches
  industry:
    domain: ""                # resolved industry
    compliance: []            # list of frameworks auto-applied
    data_model: []            # relevant entities (Patient, Transaction, Incident, etc.)
  trigger:
    event: ""                 # what kicks the agent off
    source: ""                # which system sends the trigger
    frequency: ""             # real-time | scheduled (cron) | on-demand | event-driven
  actor_today: ""             # who does this manually today
  desired_outcome: ""         # what "done" looks like
  pain_intensity: ""          # low | medium | high | critical
  urgency: ""                 # demo_now | this_week | this_sprint | next_quarter
  alternatives_considered:    # if platform was ambiguous
    - platform: ""
      reason_not_chosen: ""
  open_questions: []          # any ambiguities that the builder should flag

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Output

Produce the structured agent spec and a decoding summary:

DECODE COMPLETE
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Agent: [agent name]
Type:  [agent type]
Platform: [primary platform] → [entry point]
Industry: [domain] | Compliance: [frameworks]
Trigger: [event] from [source system] ([frequency])

Agent Spec:
[populated agent_spec YAML]

Ready for: enterprise-agent-builder
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Hand off the completed agent_spec YAML to enterprise-agent-builder.