AEGIS · Module 4 · Deploy
Artifact 4.1
Governed Workflow Automations
Library of AI-assisted workflows with autonomy tier, guardrails, checkpoints, and measurement, the operational layer where policy becomes practice.
- Client
- [CLIENT NAME]
- Engagement
- [ENGAGEMENT ID]
- Version
- v1.0
- Issued
- 2026-09-22
Delivered by TechFides under the AEGIS Governance Operating Services engagement. This document is proprietary to the client named above. Redistribution beyond the engagement steering committee requires written consent.
Purpose
Intent. Deploy is where the first three modules meet the work. Every workflow here is an explicit contract: who owns it, what it is allowed to touch, where the human is, and how success is measured.
What a governed workflow is
A governed workflow is a named, owned, documented path through which AI contributes to real work under explicit controls. Ad-hoc AI use lives under Policy 1.1, the workflows in this artifact are the sanctioned, repeatable patterns that do not require case-by-case approval.
What this artifact is not
- Not an exhaustive list of every AI interaction, those belong under the Acceptable Use Policy.
- Not technical documentation of the integration, those live in engineering systems and are referenced here.
- Not a prompt library, prompts reside in Artifact 4.2 and are referenced by workflow ID.
Autonomy Tiers
Intent. The tier is the single most important attribute on every workflow, it dictates oversight, logging, and the escalation path.
AI produces a draft; human accepts, edits, or discards every output before it leaves the workflow.
Human role · Author of record. AI never acts without explicit human commit.
Examples: Email drafting, meeting summaries, research briefs.
AI produces an output that proceeds only after a checkpoint review by a named role. Rejection is cheap; approval is logged.
Human role · Reviewer of record. Must confirm classification, factual accuracy, and policy fit at the checkpoint.
Examples: Contract redline suggestions, support-ticket responses on common categories, marketing copy for Tier-2 channels.
AI executes autonomously in production. Human oversight is by sampling and by exception, outputs are monitored, not pre-approved.
Human role · Accountable owner reviews aggregate outputs, error rate, and outliers weekly or monthly per the workflow spec.
Examples: Data enrichment, ticket routing, pricing draft generation, first-touch triage.
AI acts end-to-end on decisions with real-world effect. Permitted only with model-level evaluation, hard guardrails, and rollback.
Human role · Off the hot path. Oversees via control-plane dashboards and kill-switch authority.
Examples: Automated refunds under threshold, scheduled content publishing, inventory rebalancing within policy band.
Workflow Specification Template
Intent. Every entry in the library carries the same eleven fields. Any entry missing a field is not in service.
- Workflow ID
- WF-NN (unique, stable)
- Name
- Short, action-oriented
- Owner
- Named accountable individual
- Autonomy Tier
- T1 / T2 / T3 / T4
- Tool(s)
- References inventory IDs (Artifact 3.1)
- Data Classes
- Per Artifact 2.1: P0/P1/P2/P3 allowed
- Trigger
- Precisely what kicks off the workflow
- Outputs
- What the workflow produces and for whom
- Guardrails
- Explicit prohibitions + enforcement mechanism
- Human Checkpoint
- Where and by whom oversight happens
- Metrics
- Success + risk measures, cadence, threshold
Workflow Library
Intent. Seven example workflows spanning all four autonomy tiers. Clients typically ship 8 to 15 workflows through the AEGIS program in the first year.
Contract Review Assistant
- Owner
- General Counsel
- Tool
- Claude for Work
- Data Class
- P1 (counterparty contract)
- Trigger
- New contract uploaded to CLM with tag 'AI-review'.
- Outputs
- Redline summary, risk flags against the playbook, suggested rewrites with clause citations.
- Guardrails
- No filings, no outbound communication to counterparty.
- Privileged material excluded by CLM workflow tag.
- All citations linked to source clause, unsourced suggestions rejected.
- Human Checkpoint
- Contract attorney reviews every output before any edit is sent.
- Metrics
- Redline accuracy (vs. attorney gold standard), time saved, missed-issue rate at 90 days post-execution.
Proposal Response Copilot
- Owner
- VP Sales
- Tool
- ChatGPT Enterprise + internal knowledge base
- Data Class
- P2 (internal), no P0/P1
- Trigger
- New RFP imported to opportunity record.
- Outputs
- Draft responses to each RFP question keyed to approved proposal library, gap list for manual response.
- Guardrails
- Responses drawn only from approved library snippets, no free-form marketing claims.
- Pricing, SLA, and security sections excluded from AI generation.
- Final document regenerated from approved snippets after editor accepts.
- Human Checkpoint
- Proposal manager reviews full draft before it leaves the tool; Sales Engineering reviews technical sections.
- Metrics
- Turnaround time, win rate on AI-assisted vs. control, editor rework hours.
Support Ticket Triage
- Owner
- VP Customer
- Tool
- Zendesk AI + internal routing rules
- Data Class
- P2 (customer-submitted ticket content, scrubbed of PII).
- Trigger
- New ticket enters queue.
- Outputs
- Category, priority, owning team, suggested first response pulled from library.
- Guardrails
- PII stripped at ingress before any AI processing.
- Any ticket flagged 'regulated' routed to human queue with no AI touch.
- Suggested responses tagged as such, agent must approve before send.
- Human Checkpoint
- Weekly sample review of 50 routed tickets + misroute rate audit.
- Metrics
- Routing accuracy, first-response time, CSAT on AI-assisted vs. manual.
Engineering Code Assistant
- Owner
- VP Engineering
- Tool
- GitHub Copilot Business
- Data Class
- P2 code; P1 only in flagged private repos per policy 1.1.
- Trigger
- Developer invokes suggestion in IDE.
- Outputs
- Code suggestion inline, unit test suggestion, PR description draft.
- Guardrails
- Disabled in repos containing secrets, customer data, or unlicensed third-party code.
- Suggested dependencies auto-scanned for license + CVE before merge.
- No push directly to protected branches, review required.
- Human Checkpoint
- PR review by second engineer, AI origin noted in PR template but not a separate approval gate.
- Metrics
- PR cycle time, revert rate on AI-originated code, test coverage delta.
Meeting Notes & Action Items
- Owner
- Chief of Staff
- Tool
- Otter.ai Business
- Data Class
- P2 only. Never legal, HR, customer calls, or board.
- Trigger
- Bot invited to meeting by meeting owner.
- Outputs
- Transcript, summary, action item list attributed to named attendees.
- Guardrails
- Explicit participant consent captured at meeting start.
- Auto-stop on any statement flagged as privileged or HR-sensitive.
- 30-day retention; no long-term storage without owner action.
- Human Checkpoint
- Meeting owner edits and distributes notes. AI output is never sent unedited.
- Metrics
- Summary fidelity (spot-check), action-item hit rate, consent-capture compliance.
Pricing Draft Generation
- Owner
- VP Finance
- Tool
- Internal pricing engine + Claude API
- Data Class
- P1 (deal-specific), processed in a tenant-isolated environment.
- Trigger
- Opportunity moves to 'Pricing' stage.
- Outputs
- Draft price sheet within configured band using product rules and deal attributes.
- Guardrails
- Hard band limits enforced deterministically, AI cannot price outside.
- Any discount >15% or term >24 months routes to human approval.
- Output includes rationale and source inputs, no opaque pricing.
- Human Checkpoint
- AE reviews draft; Finance approves any exceptions; quarterly audit of band adherence.
- Metrics
- Pricing cycle time, band-adherence rate, approved-exception frequency.
Refunds Under Threshold
- Owner
- VP Customer
- Tool
- Internal automation + rules engine
- Data Class
- P1 (transaction data), internal only.
- Trigger
- Customer refund request matching policy criteria.
- Outputs
- Refund executed within policy thresholds; notification to customer.
- Guardrails
- Hard $ limit per transaction and per customer per month.
- Fraud signals auto-escalate to human queue regardless of value.
- Daily + weekly aggregate reporting with anomaly alerting.
- Human Checkpoint
- No per-transaction human review. Weekly statistical review + quarterly full audit by Finance.
- Metrics
- Rate within threshold, exception frequency, customer CSAT post-refund, abuse signals.
Lifecycle
Intent. A workflow is never 'done.' These five phases keep the library honest over time.
1 · Design
Owner drafts the spec against this template. Governance Lead reviews autonomy tier and guardrails. Engineering confirms technical feasibility.
2 · Pilot
Run in a bounded environment against synthetic or low-impact real data for a fixed window. Metrics baselined. Failure modes documented.
3 · Launch
Production deployment following sign-off by Owner, Governance Lead, and affected function head. Entry in Prompt Library (4.2) and relevant SOPs (4.3) published in the same release.
4 · Operate
Continuous telemetry against the stated metrics. Weekly review by Owner; monthly review in Governance Committee; quarterly inclusion in Board Pack (6.2).
5 · Retire
Triggered by ROI failure (Artifact 3.3), regulatory shift, or tool retirement (3.1). Access revoked, artifacts archived, SOPs updated. Lessons learned folded into the next design.
Escalation & Exceptions
Intent. When reality diverges from the spec, these paths catch it before it becomes an incident.
Owner-initiated exception
Temporary deviation from the spec to handle a specific case. Bounded in scope and time, logged, reviewed at the next governance meeting. Recurring exceptions are a signal to update the spec.
Guardrail breach
Any automated signal that the workflow operated outside its stated guardrails is a SEV-3 incident at minimum (Artifact 2.3). Runbook applies. Workflow is paused pending root cause.
Drift
Metric trends outside tolerance over two review cycles trigger mandatory re-design review. Owner has 60 days to bring the workflow back within tolerance or recommend retirement.
Regulatory Mapping
Intent. Governed workflow documentation is increasingly a line-item in customer security reviews and regulatory assurance exercises.