Non-Technical Track: Decision Frameworks for AI Agents

For Managers, PMs, and Decision-Makers

Framework 1: "Should We Use an Agent for This?"

The 4-Question Filter (for non-technical stakeholders)

Q1: Can a human do this task with clear step-by-step instructions?

Yes → Could be agentizable
No → Humans struggle too; don't agentize

Q2: Does the task have clear success criteria?

Yes → We'll know if the agent succeeded
No → You can't evaluate an agent if you can't define "done"

Q3: What's the cost of failure?

Low (wrong suggestion, formatting error) → Full autonomy possible
Medium (wrong code, wrong data) → Human review required
High (financial loss, safety, legal) → Human-in-the-loop mandatory

Q4: How many times does this task need to be done?

Once → Just do it manually
10x → Build a script
100x → Build an agent
1000x → Build a multi-agent system

Decision Matrix

               Low Failure Cost    High Failure Cost
──────────────┼─────────────────────────────────────
High Volume   │  Build agent      │  Agent + HITL
Low Volume    │  Do it manually   │  Do it manually

Framework 2: The 4 Agent Design Patterns (Andrew Ng)

1. Reflection

The agent checks its own work before presenting it.

Agent writes code → reviews own code → fixes issues → presents final

Business value: Reduces human review time by 40-60%. Catches obvious errors before they reach you.

2. Tool Use

The agent uses external tools to gather information or take actions.

Agent reason → call search API → read results → call database → compose answer

Business value: Agents can access real-time data, not just training data. Turns a static LLM into a live system.

3. Planning

The agent decomposes a complex request into sub-steps.

"Build a landing page" → plan steps (1. Design, 2. Code, 3. Test, 4. Deploy)
                         → execute each step → verify results

Business value: Complex tasks get done without step-by-step human instruction. The agent figures out the steps.

4. Multi-Agent Collaboration

Multiple specialized agents work together on different parts of a task.

PM agent plans → Designer creates mockup → Frontend builds → QA tests

Business value: Each agent is an expert in its domain. Together they outperform one generalist agent at a fraction of the cost.

Framework 3: Cost Estimation for Agent Projects

The 3x Rule

A production agent costs 3x your prototype estimate. Plan accordingly.

Phase	Cost Multiplier	What's Included
Prototype (works on happy path)	1x	Single agent, one model, no safety
Production (handles edge cases)	2x	Retry logic, error handling, testing
Production+ (operational)	3x	Monitoring, security, CI/CD, budgets

Quick Cost Calculator

Per-task cost = (input_tokens × input_price + output_tokens × output_price) × retry_rate × overhead

Where:
  retry_rate = 1.5 (typical for agent systems)
  overhead = 3.0 (production multiplier)

Example:
  Agent session: 4000 input tokens, 1000 output tokens
  Model: Claude Sonnet ($3/$15 per M tokens)
  
  Base cost: (4000 × $3/M + 1000 × $15/M) = $0.012 + $0.015 = $0.027
  With retries: $0.027 × 1.5 = $0.041
  Production cost: $0.041 × 3.0 = $0.123 per task
  
  At 1000 tasks/month: $123/month
  At 10000 tasks/month: $1230/month

Cost by Autonomy Level

Level	Cost/Task	Human Time/Task	Best For
No agent	$0	30 min	One-off tasks
AI-assisted	$0.01-0.05	10 min	Human does the work, AI helps
Agent with review	$0.05-0.30	5 min	Agent works, human reviews
Full autonomy	$0.10-1.00	0 min	Agent works, human audits
Multi-agent	$0.50-5.00	2 min	Complex workflows, human supervises

Framework 4: Autonomy vs Risk Matrix

                    Low Risk              Medium Risk           High Risk
                    (suggestions,         (code gen, data       (financial, medical,
                     content gen)          processing)           infrastructure)
─────────────┼─────────────────────────────────────────────────────────────
Tier 1:      │ Full autonomy           │ Agent + review      │ Human only
Simple       │                         │                     │
             │                         │                     │
Tier 2:      │ Agent + review          │ Agent + verifier +  │ Human + agent
Complex      │                         │ human spot-check    │ as tool
             │                         │                     │
Tier 3:      │ Multi-agent + audit     │ Multi-agent +       │ Not recommended
Strategic    │                         │ verifier + human    │ for agents

Framework 5: The "Build vs Buy" Decision

Build an Agent When:

Your task is unique to your business
You need to keep data in-house
No existing tool solves the problem
You have the engineering capacity

Buy/Rent When:

It's a common task (customer support, code review, monitoring)
A SaaS product already exists
You don't have AI engineering expertise
Speed to market matters more than customization

Hybrid When:

Core task is unique, but supporting tasks are common
You can build a small agent that calls existing SaaS tools

Framework 6: Measuring Success

Leading Indicators (week 1-4)

Task completion rate (is the agent finishing its work?)
Human review time reduction
Cost per task vs cost of human doing it
Error rate (how often does the agent need correction?)

Lagging Indicators (month 2-3+)

Time saved per week
Quality compared to human baseline
Number of tasks automated vs manual
Maintenance burden (how often does the agent break?)

Red Flags

Agent costs more than the human it replaces
Agent requires more oversight than doing it yourself
Agent breaks every time the model updates
Team doesn't trust the agent's output

Framework 7: Governance & Compliance Quick Reference

Regulatory Requirements by Region

Requirement	EU (AI Act)	California (SB 1047)	UK/Japan
Human oversight	Mandatory for high-risk	Mandatory kill-switch	Voluntary
Audit trails	Required	Required	Recommended
Incident reporting	Required	Required	Voluntary
Disclosure (AI-generated)	Required	Required	Recommended
Data privacy (GDPR)	Full compliance	Similar	Different standard

Minimum Viable Governance

For teams not in regulated industries, start with:

Every agent action is logged with timestamp, agent, input, output
Human review gate for destructive operations (deletes, writes, spending money)
Monthly cost report per agent per task type
Quarterly security review — has the agent's behavior changed?

Summary: Decision Flow

1. Can this task be automated?
   ├── No → Do it manually
   └── Yes →
2. What's the failure cost?
   ├── High → Human-in-the-loop mandatory
   └── Low →
3. What's the volume?
   ├── <10/month → Do it manually
   ├── 10-100/month → Build a script
   └── >100/month → Build an agent
4. How many specialists needed?
   ├── One → Single agent
   └── Multiple → Multi-agent system
5. What's the budget?
   ├── <$50/month → Use cheapest model (Gemini Flash)
   ├── $50-500/month → Cascade routing (mix models)
   └── >$500/month → Multi-agent with verification

Non-Technical Track: Decision Frameworks for AI Agents ​

For Managers, PMs, and Decision-Makers ​

Framework 1: "Should We Use an Agent for This?" ​

The 4-Question Filter (for non-technical stakeholders) ​

Decision Matrix ​

Framework 2: The 4 Agent Design Patterns (Andrew Ng) ​

1. Reflection ​

2. Tool Use ​

3. Planning ​

4. Multi-Agent Collaboration ​

Framework 3: Cost Estimation for Agent Projects ​

The 3x Rule ​

Quick Cost Calculator ​

Cost by Autonomy Level ​

Framework 4: Autonomy vs Risk Matrix ​

Framework 5: The "Build vs Buy" Decision ​

Build an Agent When: ​

Buy/Rent When: ​

Hybrid When: ​

Framework 6: Measuring Success ​

Leading Indicators (week 1-4) ​

Lagging Indicators (month 2-3+) ​

Red Flags ​

Framework 7: Governance & Compliance Quick Reference ​

Regulatory Requirements by Region ​

Minimum Viable Governance ​

Summary: Decision Flow ​