agentic-ai-engineering/site/modules/m6-economics.md

# Module 6: Economics & Evaluation

## Lesson 6.0: The Compute Advantage Equation

**Core insight**: Your value as an agentic engineer scales with the amount of compute you can harness effectively.

### The Equation

```
Compute Advantage = (Compute Scaling × Autonomy) ÷ (Time + Effort + Monetary Cost)
```

| Variable | What It Means | How to Improve It |
|----------|---------------|-------------------|
| **Compute Scaling** | How much AI compute you can throw at problems | More agents, better models, larger context windows |
| **Autonomy** | How much the agent does without your intervention | Better prompts, better tools, verification layers |
| **Time** | How long it takes to get results | Faster models, parallel execution, fewer iterations |
| **Effort** | How much you have to craft prompts/instructions | Reusable skills, templates, mental models |
| **Monetary Cost** | What you pay for API calls | Cascade routing, cheaper models for simple steps |

### What It Tells You

- **High Compute Advantage** = You get more output for less input. You're leveraging agents effectively.
- **Low Compute Advantage** = You're spending too much time/cost for too little gain. Fix the bottleneck variable.

### Real-World Application (Your Stack)

| Tool | Compute Scaling | Autonomy | Time | Effort | Cost | Advantage |
|------|:---:|:---:|:---:|:---:|:---:|:---:|
| Claude Code (lead) | 8 | 7 | 6 | 5 | 4 | (8×7)÷(6+5+4)=3.7 |
| Pi Agent (custom) | 7 | 8 | 7 | 6 | 6 | (7×8)÷(7+6+6)=2.9 |
| OpenCode (OSS) | 5 | 5 | 7 | 7 | 9 | (5×5)÷(7+7+9)=1.1 |
| Gemini Flash (fast) | 4 | 3 | 9 | 8 | 9 | (4×3)÷(9+8+9)=0.5 |

Higher score = more output per unit of investment. Use this to decide which tool for which task.

### How to Optimize

1. **Improve numerator**: Run more agents in parallel (P-threads), increase autonomy with verification
2. **Reduce denominator**: Use cascade routing (cheap model for simple steps), reuse skills/mental models
3. **Track over time**: Your Compute Advantage should increase as you build better harnesses and mental models

---

## Lesson 6.1: LLM Pricing Landscape 2026

### Per-Million-Token Pricing

| Model | Input ($/M) | Output ($/M) | Best For |
|-------|-------------|--------------|----------|
| Gemini 2.5 Flash | $0.15 | $0.60 | High-volume, simple tool calls |
| DeepSeek V3 | $0.27 | $1.10 | Structured tasks, batch processing |
| DeepSeek R1 | $0.55 | $2.19 | Reasoning-heavy single steps |
| Gemini 2.5 Pro | $1.25 | $5.00 | Long-context sessions (1M+ tokens) |
| GPT-4o | $2.50 | $10.00 | Balanced cost/quality |
| Claude Sonnet 4 | $3.00 | $15.00 | General agentic reasoning |
| GPT-5 | $10.00 | $40.00 | Frontier research, complex plans |
| Claude Opus 4 | $15.00 | $75.00 | Complex multi-step agent orchestration |

### Price Range: 100x Difference

Cheapest (Gemini Flash) to most expensive (Claude Opus) is a **100x multiplier**. Choosing the right model for each step is your highest-leverage cost optimization.

---

## Lesson 6.2: Cascade Routing

### The Pattern

Don't use one model for everything. Route different steps to different models:

```
Step 1: Retrieve context → Gemini Flash ($0.15/M input)
Step 2: Analyze → Claude Sonnet ($3/$15)
Step 3: Make decision → Claude Opus ($15/$75)
Step 4: Format output → Gemini Flash ($0.15/$0.60)
```

### Savings Profile

| Pattern | Cost/Task | Savings |
|---------|-----------|---------|
| All Opus | $2.50 | Baseline |
| Cascade (Flash → Sonnet → Opus → Flash) | $0.85 | 66% savings |
| All Sonnet | $0.50 | 80% savings (but quality loss on complex steps) |

### Implementation

```python
def route_task(task_complexity: str) -> str:
    if task_complexity == "retrieval":
        return "gemini-2.5-flash"
    elif task_complexity == "analysis":
        return "claude-sonnet-4"
    elif task_complexity == "decision":
        return "claude-opus-4"
    elif task_complexity == "formatting":
        return "gemini-2.5-flash"
```

---

## Lesson 6.3: Cost Per Session Math

### Where Costs Come From

| Component | Share | Notes |
|-----------|-------|-------|
| Output tokens | ~70% | Model generation is most expensive |
| Input tokens | ~20% | Context + tool results |
| Cached tokens | ~10% | Can be zero if not configured |

### The Multipliers

| Factor | Multiplier | Why |
|--------|-----------|-----|
| Retry rate | 1.2-2.0x | Failed tool calls retry |
| Tool overhead | 3-5x per tool call | Planning + execution + error recovery + result parsing |
| Context growth | 1.5x per 10 turns | Every turn adds tokens to context |

### The 3x Rule

**Production agent costs 3x your prototype estimate.**

1x = ideal path (everything works first time)
2x = retries + edge cases
3x = monitoring + error handling + observability overhead

### Quick Estimation

```python
def estimate_cost(turns: int, avg_tokens_per_turn: int, model_price_per_m: float):
    """Very rough estimate."""
    base = turns * avg_tokens_per_turn * model_price_per_m / 1_000_000
    retry = base * 1.5
    overhead = base * 3.0
    return {"base": base, "with_retries": retry, "production": overhead}
```

---

## Lesson 6.4: Agent Evaluation Metrics

### The Key Metrics

| Metric | What It Measures | Target |
|--------|-----------------|--------|
| **pass@k** | % of k attempts where at least one succeeds | >80% |
| **pass^k** | % where ALL k attempts succeed (consistency) | >60% |
| **Tool Call Accuracy** | Correct tool + correct params | >90% |
| **Task Completion Rate** | End-to-end success | >70% |
| **Cost Per Task** | Total API cost per unit | Varies |
| **Loop Efficiency** | Steps taken vs optimal | <2x overhead |
| **Grind Rate** | % of attempts that re-run identical code | <5% |

### pass@k Explained

```
pass@k = probability that at least one of k attempts succeeds

k=1: 60% pass rate (single attempt)
k=3: 1 - (0.4)^3 = 93.6% (best of 3)
k=5: 1 - (0.4)^5 = 98.9% (best of 5)
```

Higher k = higher reliability but higher cost. The trade-off is the core optimization problem.

---

## Lesson 6.5: Automated Evaluation

### Golden Dataset Approach

```python
eval_cases = [
    {
        "input": "Find the user with email john@example.com",
        "expected_tool": "query_database",
        "expected_params": {"query": "SELECT * FROM users WHERE email = 'john@example.com'"},
        "expected_output_contains": ["john@example.com"],
        "weight": 1.0
    }
]
```

### VCR-Style Recording

For non-deterministic tool results (search, API calls), record the response once, then replay it deterministically:

```python
# Record mode: capture real responses
# Replay mode: use recorded responses
# Test: verify agent makes correct decisions with known data
```

---

## Lesson 6.6: A/B Testing Agents

### Canary Deployment

1. Route 5% of traffic to new agent config
2. Compare against 95% on current config
3. Metrics: success rate, cost, latency, loop depth
4. If new config wins on all metrics → roll out to 100%
5. If new config loses → rollback, investigate

### What to A/B Test

- System prompt wording
- Model selection
- Temperature settings
- Tool descriptions
- Iteration limits

---

## Lesson 6.7: Human Evaluation

### What Automated Evals Miss

1. **Quality of reasoning** — agent made right decision for wrong reasons?
2. **Tone and style** — output technically correct but poorly written?
3. **Edge cases** — agent handled happy path but not real-world variation?
4. **Hallucination cascades** — plausible-looking but wrong intermediate steps?

### Spot-Check Sampling

Review 5-10% of all agent sessions manually. Focus on:
- Sessions with high cost (>2x average)
- Sessions with tool loops
- Sessions where automated eval scored low
- Random sample for baseline

---

## Lesson 6.7b: Real-World Cost Scenarios

### Scenario A: Research Agent (High Token Burn)

```
Pattern: Agent reads 20 web pages, synthesizes report
Cost breakdown:
├── 20 web page reads @ 2K tokens each = 40K input tokens
├── 5 LLM reasoning turns @ 4K tokens = 20K tokens
├── 1 report generation @ 8K output tokens = 8K output tokens
├── Claude Sonnet: ~$0.08/run
├── Claude Haiku: ~$0.02/run (80% cheaper, 90% as good for extraction)
└── Cascade: Haiku for reads, Sonnet for synthesis → $0.03/run
```

**The cascade saves 62%** on this exact pattern. Use cheap models for bulk work, expensive models for synthesis.

### Scenario B: Code Generation (Iterative)

```
Pattern: Agent writes code, tests, fixes, repeats
Cost without optimization:
├── Average: 8 iterations × 6K tokens = 48K tokens
├── Cost: ~$0.35 per feature
├── With guardrails (limit to 3 iterations): $0.13 per feature
└── Savings: 63%
```

### Scenario C: Always-On Brand Monitor

```
Pattern: Scans 50 sources every 5 minutes, 24/7
Cost without optimization:
├── 288 runs/day × $0.08 = $23.04/day = $691/month
├── With cascade + dedup + scheduling: $4.15/day = $125/month
└── Savings: 82%
```

### The 80/20 Rule

90% of cost savings come from three changes:
1. **Model cascade** — use cheap models for routine work (saves 50-80%)
2. **Iteration limits** — cap loops at 3-5 turns (saves 40-60%)
3. **Deduplication** — don't re-read the same context (saves 20-30%)

Do these three first before any other optimization.

---

## Lesson 6.7c: Monitoring Agent Economics in Production

Once your agent is deployed, you need to track costs in real-time. Here's what to monitor and how.

### Dashboard Metrics

```
Cost Dashboard (example)
├── Cost per session (avg, p95, max)
├── Cost per tool call (avg by tool type)
├── Cost per model tier (Haiku vs Sonnet vs Opus)
├── Loop depth distribution (how many turns do sessions take?)
├── Cost by hour of day (when are agents most expensive?)
└── Monthly burn rate (projected vs actual)
```

### Setting Up Cost Tracking

The simplest approach: log every LLM call with its cost to a JSONL file.

```python
# cost-logger.py — append-only cost tracking
import json, time, os

LOG_FILE = "cost-log.jsonl"

def log_llm_call(model, prompt_tokens, output_tokens, cost_cents):
    entry = {
        "timestamp": time.time(),
        "model": model,
        "prompt_tokens": prompt_tokens,
        "output_tokens": output_tokens,
        "cost_cents": cost_cents,
        "session_id": os.environ.get("SESSION_ID", "unknown"),
    }
    with open(LOG_FILE, "a") as f:
        f.write(json.dumps(entry) + "\n")
```

### Cost Alerts

Set up automated alerts for cost anomalies:

| Alert | Threshold | Action |
|-------|-----------|--------|
| Session cost exceeded | >$2.00 | Kill session, notify operator |
| Daily budget warning | >80% of daily budget | Notify operator |
| Cost spike detection | >3x average for this agent | Investigate loop behavior |
| Model tier drift | >10% of calls using Opus | Check cascade routing config |

### The Cost-to-Value Ratio

Not all costs are bad. An expensive agent that ships features is more valuable than a cheap agent that does nothing. Track:

- **Cost per task completed** (not just cost per call)
- **Revenue generated per agent session** (if applicable)
- **Time saved vs human doing the same task**
- **Error rate** (cheap agents that make mistakes cost more in debugging time)

---

## Lab 6.8: Build an Eval Harness

**Objective**: Create golden Q&A pairs + automated pass/fail scoring.

**Starter**: `course/labs/L6-eval-harness/starter.py`

## Lab 6.9: Cost Optimization

**Objective**: Profile a session, identify savings, implement cascade routing.

**Starter**: `course/labs/L6-cost-optimization/starter.py`