Real-World Agent Use Cases

AI agents can handle support queries that require investigation and action:

Capabilities:

• Understand customer issue from conversation
• Look up relevant account information
• Check order status, subscription details
• Diagnose problems using knowledge base
• Take actions (issue refunds, update settings)
• Escalate when appropriate

Example workflow:

1. Customer: "My order hasn't arrived"
2. Agent looks up order by customer ID
3. Checks shipping status in logistics system
4. Finds delay due to weather
5. Offers compensation and updated ETA
6. Customer accepts, agent applies credit

What makes this work:

• Clear scope (support requests)
• Defined actions (lookup, credit, escalate)
• Knowledge base for diagnostics
• Human escalation path

Metrics to track:

• Resolution rate without escalation
• Customer satisfaction scores
• Time to resolution
• Cost per ticket vs. human agents

Agents for sales research, outreach, and pipeline management:

Lead research agents:

• Take a company name or domain
• Research company details (size, industry, news)
• Find relevant contacts
• Identify potential needs/pain points
• Prepare briefing for sales rep

Outreach personalization:

• Analyze prospect's company and role
• Research recent news or achievements
• Draft personalized email
• Human reviews and sends
• Track responses and optimize

CRM hygiene:

• Review meeting notes
• Extract key information
• Update CRM fields
• Create follow-up tasks
• Flag deals at risk

What works well:

• Research tasks (gathering public info)
• Data entry (structured extraction)
• Draft creation (human edits final)

What needs caution:

• Direct customer communication (high stakes)
• Pricing decisions (needs human approval)
• Contract terms (legal review required)

Automating operational workflows that require judgment:

Data processing:

• Process incoming documents
• Extract relevant information
• Validate against business rules
• Route to appropriate handlers
• Flag exceptions for review

Reporting:

• Gather data from multiple sources
• Analyze patterns and anomalies
• Generate narrative explanations
• Create visualizations
• Distribute to stakeholders

Coordination:

• Monitor project status
• Identify blockers or delays
• Send reminders and follow-ups
• Update documentation
• Escalate issues

Vendor/Partner Management:

• Monitor deliverables
• Track SLA compliance
• Handle routine inquiries
• Prepare review materials

Key success factors:

• Well-defined processes
• Clear data access
• Defined escalation paths
• Measurable outcomes

Agents for gathering and synthesizing information:

Market research:

• Monitor competitors
• Track industry news
• Summarize developments
• Identify trends and patterns
• Alert on significant changes

Due diligence:

• Research companies for investment/partnership
• Gather public information
• Identify red flags
• Prepare summary reports

Technical research:

• Explore solutions to technical problems
• Evaluate tools and services
• Summarize documentation
• Compare alternatives

Internal knowledge:

• Search company documentation
• Find relevant past decisions
• Summarize policy changes
• Answer employee questions

Research agent characteristics:

• Heavy use of search and retrieval tools
• Synthesis across multiple sources
• Citation and source tracking
• Confidence indicators on findings

How to choose your first agent use case:

Good starting points:

• High volume (justifies investment)
• Moderate complexity (simple enough to get right)
• Clear success criteria (can measure if it works)
• Reversible actions (mistakes aren't catastrophic)
• Existing data/tools (don't need to build everything)

Avoid starting with:

• Low volume (hard to justify, hard to learn)
• Extremely complex (likely to fail)
• No clear success metric (can't tell if it's working)
• Irreversible high-stakes (too risky for early agents)
• Missing infrastructure (too much to build at once)

Evaluation questions:

1. What would a human do to complete this task?
2. What information and tools would they need?
3. Where would they need to use judgment?
4. What could go wrong and how bad would it be?
5. How would you know if the agent succeeded?

Pilot approach:

• Start with shadow mode (agent suggests, human acts)
• Measure accuracy and time savings
• Gradually increase autonomy
• Expand scope as confidence grows

Build one successful agent, learn from it, then expand.