> ## Documentation Index
> Fetch the complete documentation index at: https://docs.adenhq.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Defining Goals

> Define what your agent should achieve with success criteria and constraints

Goals are the foundation of goal-driven agents in Aden. They define **WHAT** your agent should achieve, not **HOW**.

<Tip>
  For the conceptual foundation behind goals and success criteria, see [Outcome-Driven Development](/building-agent/concepts/outcome-driven-development).
</Tip>

## What is a Goal?

A Goal contains:

* **Success Criteria** - Measurable conditions that define success
* **Constraints** - Boundaries the agent must respect
* **Context** - Additional information for decision-making

```python theme={null}
from framework.graph.goal import Goal, SuccessCriterion, Constraint

goal = Goal(
    id="my-goal",
    name="My Agent Goal",
    description="What this agent should accomplish",
    success_criteria=[...],
    constraints=[...],
)
```

## Defining Success Criteria

Success criteria are **measurable conditions** that determine if your agent succeeded.

### Structure

```python theme={null}
SuccessCriterion(
    id="unique-id",
    description="Human-readable description",
    metric="how to measure",
    target="target value",
    weight=1.0,  # Relative importance (0-1)
)
```

### Metric Types

| Metric            | Use Case                             | Example Target     |
| ----------------- | ------------------------------------ | ------------------ |
| `output_contains` | Check if output has specific content | `"success"`        |
| `output_equals`   | Exact match                          | `"expected_value"` |
| `llm_judge`       | LLM evaluates quality                | `">= 0.8"`         |
| `custom`          | Custom evaluation function           | varies             |

### Example

```python theme={null}
success_criteria = [
    SuccessCriterion(
        id="completeness",
        description="Response covers all requested topics",
        metric="llm_judge",
        target=">= 0.9",
        weight=0.5,
    ),
    SuccessCriterion(
        id="accuracy",
        description="Information is factually correct",
        metric="llm_judge",
        target=">= 0.95",
        weight=0.5,
    ),
]
```

## Defining Constraints

Constraints are **boundaries** your agent must respect during execution.

### Constraint Types

| Type   | Meaning                | Effect              |
| ------ | ---------------------- | ------------------- |
| `hard` | Must not violate       | Violation = failure |
| `soft` | Should avoid violating | Violation = warning |

### Categories

* `time` - Execution time limits
* `cost` - Token/API cost limits
* `safety` - Safety requirements
* `scope` - What the agent should/shouldn't do
* `quality` - Quality requirements

### Example

```python theme={null}
constraints = [
    Constraint(
        id="no-external-apis",
        description="Do not call external APIs without user approval",
        constraint_type="hard",
        category="safety",
    ),
    Constraint(
        id="response-length",
        description="Keep responses under 500 words",
        constraint_type="soft",
        category="quality",
    ),
]
```

## Complete Example

Here's a complete goal for a research agent:

```python theme={null}
from framework.graph.goal import Goal, SuccessCriterion, Constraint

research_goal = Goal(
    id="research-agent-goal",
    name="Technical Research Agent",
    description="Research technical topics and provide comprehensive summaries",
    
    success_criteria=[
        SuccessCriterion(
            id="topic-coverage",
            description="Covers all major aspects of the topic",
            metric="llm_judge",
            target=">= 0.9",
            weight=0.4,
        ),
        SuccessCriterion(
            id="source-quality",
            description="Uses credible sources",
            metric="llm_judge",
            target=">= 0.8",
            weight=0.3,
        ),
        SuccessCriterion(
            id="clarity",
            description="Output is clear and well-structured",
            metric="llm_judge",
            target=">= 0.85",
            weight=0.3,
        ),
    ],
    
    constraints=[
        Constraint(
            id="verify-sources",
            description="All information must be from verifiable sources",
            constraint_type="hard",
            category="quality",
        ),
        Constraint(
            id="no-speculation",
            description="Do not include unverified speculation",
            constraint_type="hard",
            category="safety",
        ),
        Constraint(
            id="time-limit",
            description="Complete research within 5 minutes",
            constraint_type="soft",
            category="time",
        ),
    ],
    
    required_capabilities=["web_search", "llm"],
    
    context={
        "domain": "technology",
        "audience": "developers",
    },
)
```

## Best Practices

<CardGroup cols={2}>
  <Card title="Do" icon="check">
    * Use **specific, measurable** success criteria
    * Include **3-5 success criteria** with different weights
    * Define **hard constraints** for critical requirements
    * Add **context** to help the agent make better decisions
  </Card>

  <Card title="Don't" icon="xmark">
    * Use vague criteria like "do a good job"
    * Skip constraints entirely
    * Set all weights to 1.0 (use relative importance)
    * Forget to specify required capabilities
  </Card>
</CardGroup>

## Next Steps

<CardGroup cols={2}>
  <Card title="Node Types" icon="cube" href="/building/nodes">
    Learn about LLM, Router, Function, and Human nodes
  </Card>

  <Card title="Edge Configuration" icon="arrow-right" href="/building/edges">
    Configure success, failure, and conditional edges
  </Card>
</CardGroup>