How to Run Legal Intake With AI Agents: The Key to Speed, Visibility, and Alignment

Gartner forecasts corporate legal tech spend will triple by 2025, yet many departments still manage intake from an unstructured inbox. The gap isn’t more tools—it’s an operating model. AI agents, paired with clear playbooks, turn intake from firefighting into a reliable system for speed, visibility, and alignment.

What AI Agents Actually Do in Legal Intake

Think of intake like air traffic control. Requests land from everywhere—email, Slack, Salesforce, procurement portals. AI agents act as controllers: they read each request, classify it, apply a playbook, and route it with context. They don’t replace attorneys; they clear the runway so attorneys focus on judgment calls.

In practice, an intake agent can:

- Parse a request and identify type, urgency, and business owner

- Check policy thresholds (e.g., customer NDA, low risk, approved template)

- Generate a draft with the right playbook, attach fallback clauses, and log rationale

- Route to the right lane: auto-approve, self-serve, or human review

- Track status and outcomes to a dashboard, building a reusable knowledge base

The payoff is immediate: faster first responses, fewer ad-hoc pings, and a reliable audit trail. And because each decision is captured, your playbooks get sharper over time—knowledge doesn’t disappear in DMs.

A Blueprint You Can Launch in Weeks

You don’t need a moonshot. Start with one high-volume, low-complexity workflow and make it boringly reliable.

1) Pick a target use case

- NDAs, marketing asset reviews, or low-risk vendor assessments are ideal. Frequency and policy clarity matter more than edge cases.

2) Turn policy into a playbook

- Write a one-page decision tree: triggers, thresholds, approved fallbacks, and when to escalate. Keep it rule-first, then layer AI for interpretation.

3) Connect where work starts

- Wire up email, Slack/Teams, and your CRM or procurement system so requests flow into a single intake form. Require business context up front (counterparty, value, region, deadline).

4) Configure guardrails

- Define which paths the agent may auto-resolve (template + no changes), which require human sign-off, and which must be escalated immediately. Keep humans in the loop where risk concentrates.

5) Instrument the workflow

- Auto-assign owners based on domain, set SLAs for first response, and expose a request tracker for the business. Push status updates back to the channel where the request began.

On Sandstone, this looks like: a no-code intake form, an AI agent that classifies the request and applies your playbook, a routing layer to Slack/Teams and e-sign, and a knowledge layer that records decisions. Strength through layers; crafted precision; natural integration.

The Scoreboard: Prove It Works in 30 Days

If it’s not on a scoreboard, it’s a belief, not an operating model. Track these metrics from day one:

- Cycle time: request to resolution, by request type

- First-response SLA: percentage met and average time to first touch

- Auto-resolve rate: percentage handled via self-serve or auto-approve

- Rework and turnbacks: how often work bounces due to missing info or wrong lane

- Policy adherence: decisions aligned to playbooks, with auditable rationale

- Stakeholder satisfaction: quick pulse (e.g., post-close CSAT) from business partners

Aim for simple targets on your first use case: 70% first responses under one hour, 40–60% auto-resolve on NDAs/templates, and a visible queue that any stakeholder can check without DM’ing Legal.

From One Workflow to a Living Operating System

The real lift isn’t the first workflow—it’s what happens next. Each resolved request adds a layer: data about the scenario, the decision, and the outcome. Over time, your system gains:

- Better classification: the agent recognizes patterns in business context and risk

- Stronger playbooks: real-world exceptions become documented pathways

- Cleaner integrations: handoffs to sales, finance, and procurement get standardized

That layered knowledge turns repeatable work into self-serve and makes complex matters easier to scope. You move from reactive support to proactive guidance. Legal becomes the connective tissue—aligning teams, accelerating deals, and reinforcing trust.

On Sandstone, every intake, triage, and decision compounds in the knowledge layer. Playbooks become living assets. Workflows are modular, so you can expand from NDAs to DPAs, marketing claims review, open-source approvals, or DPIAs without rebuilding your foundation.

Actionable Next Step: Ship an NDA Autopilot Sprint

Block two weeks. In week one, run a 60-minute whiteboard with sales, procurement, and Legal to map the NDA decision tree: template types, redlines you’ll accept, and non-starters. In week two, stand up a single intake form, connect Slack/Teams and e-sign, and enable an agent to:

- Classify NDA type and route to the correct template

- Enforce policy: auto-approve when no changes, escalate on risky edits

- Log rationale, clauses used, and cycle time to a dashboard

Announce the new lane to sales with a simple promise: requests go in one door, status is visible, and standard NDAs close same day. Measure, learn, iterate.

Legal ops isn’t about heroics; it’s about systems that scale. AI agents plus crisp playbooks turn chaos into a cadence. Build it once, make it visible, and let your knowledge compound. That’s how Legal stops being a bottleneck and becomes the bedrock of growth and trust.