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
- 2026-05-18
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 8–15 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.