Frontier Capabilities
Module 9: Learning Objectives
By the end of this module, you will:
- ✓ Understand self-improving and meta-learning agents
- ✓ Explore constitutional AI and debate systems
- ✓ Recognize open problems in alignment and interpretability
- ✓ Identify frontier research directions
- ✓ Contribute to cutting-edge agent research
Introduction to Frontier Research
Frontier capabilities represent the cutting edge of agent research—capabilities that are emerging but not yet fully realized. This section explores what’s possible and what’s coming next.
What Makes Capabilities “Frontier”?
Characteristics:
- Recently demonstrated in research
- Not yet widely deployed
- Significant technical challenges
- High potential impact
- Active research area
Categories:
- Self-improvement and meta-learning
- Tool creation and modification
- Abstract reasoning
- Long-horizon planning
- Multi-agent emergence
Self-Improvement and Meta-Learning
Self-Modifying Agents
from typing import Dict, List, Callable
import ast
class SelfImprovingAgent:
"""Agent that can modify its own code"""
def __init__(self):
self.client = openai.OpenAI()
self.code_history = []
self.performance_history = []
def analyze_performance(self, task_results: List[Dict]) -> Dict:
"""Analyze agent's performance"""
success_rate = sum(1 for r in task_results if r["success"]) / len(task_results)
avg_time = sum(r["time"] for r in task_results) / len(task_results)
return {
"success_rate": success_rate,
"avg_time": avg_time,
"total_tasks": len(task_results)
}
def identify_weaknesses(self, performance: Dict) -> List[str]:
"""Identify areas for improvement"""
weaknesses = []
if performance["success_rate"] < 0.8:
weaknesses.append("low_success_rate")
if performance["avg_time"] > 10:
weaknesses.append("slow_execution")
return weaknesses
def generate_improvement(self, current_code: str, weaknesses: List[str]) -> str:
"""Generate improved version of code"""
prompt = f"""Improve this agent code to address these weaknesses: {weaknesses}
Current code:
```python
{current_code}
Provide improved code that:
- Maintains all functionality
- Addresses identified weaknesses
- Includes comments explaining changes
Improved code:“”“
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.3
)
return self.extract_code(response.choices[0].message.content)
def validate_improvement(self, new_code: str) -> bool:
"""Validate improved code"""
try:
# Parse to check syntax
ast.parse(new_code)
# Run safety checks
if self.contains_unsafe_operations(new_code):
return False
return True
except SyntaxError:
return False
def contains_unsafe_operations(self, code: str) -> bool:
"""Check for unsafe operations"""
unsafe_patterns = [
"exec(", "eval(", "__import__",
"os.system", "subprocess"
]
return any(pattern in code for pattern in unsafe_patterns)
def self_improve(self, task_results: List[Dict]) -> Dict:
"""Self-improvement cycle"""
# Analyze performance
performance = self.analyze_performance(task_results)
self.performance_history.append(performance)
# Identify weaknesses
weaknesses = self.identify_weaknesses(performance)
if not weaknesses:
return {"improved": False, "reason": "No weaknesses found"}
# Get current code
current_code = self.get_current_code()
# Generate improvement
improved_code = self.generate_improvement(current_code, weaknesses)
# Validate
if not self.validate_improvement(improved_code):
return {"improved": False, "reason": "Validation failed"}
# Store
self.code_history.append({
"code": improved_code,
"weaknesses_addressed": weaknesses,
"timestamp": time.time()
})
return {
"improved": True,
"weaknesses_addressed": weaknesses,
"version": len(self.code_history)
}
def get_current_code(self) -> str:
"""Get current agent code"""
# In practice, would read actual code
return "def process(input): return input"
def extract_code(self, text: str) -> str:
"""Extract code from response"""
import re
pattern = r'```python\n(.*?)```'
matches = re.findall(pattern, text, re.DOTALL)
return matches[0] if matches else text
Usage
agent = SelfImprovingAgent()
Simulate task results
results = [ {“success”: True, “time”: 5.2}, {“success”: False, “time”: 12.1}, {“success”: True, “time”: 6.8} ]
Self-improve
improvement = agent.self_improve(results) print(f“Improved: {improvement}“)
### Recursive Self-Improvement
```python
class RecursiveSelfImprovement:
"""Agent that recursively improves itself"""
def __init__(self, max_iterations: int = 5):
self.max_iterations = max_iterations
self.client = openai.OpenAI()
self.versions = []
def improve_recursively(self, initial_code: str, test_suite: List[Dict]) -> Dict:
"""Recursively improve code"""
current_code = initial_code
current_score = self.evaluate_code(current_code, test_suite)
print(f"Initial score: {current_score:.2f}")
for iteration in range(self.max_iterations):
print(f"\nIteration {iteration + 1}:")
# Generate improvement
improved_code = self.generate_improvement(current_code, current_score)
# Evaluate
new_score = self.evaluate_code(improved_code, test_suite)
print(f"New score: {new_score:.2f}")
# Check if improved
if new_score > current_score:
print("✓ Improvement accepted")
current_code = improved_code
current_score = new_score
self.versions.append({
"iteration": iteration + 1,
"code": current_code,
"score": current_score
})
else:
print("✗ No improvement, stopping")
break
return {
"final_code": current_code,
"final_score": current_score,
"iterations": len(self.versions),
"improvement": current_score - self.evaluate_code(initial_code, test_suite)
}
def evaluate_code(self, code: str, test_suite: List[Dict]) -> float:
"""Evaluate code quality"""
# Run tests
passed = 0
for test in test_suite:
try:
# Execute code with test input
result = self.execute_code(code, test["input"])
if result == test["expected"]:
passed += 1
except:
pass
return passed / len(test_suite) if test_suite else 0
def generate_improvement(self, code: str, current_score: float) -> str:
"""Generate improved version"""
prompt = f"Improve this code (current score: {current_score:.2f}):\n\n{code}\n\nMake it more efficient, readable, and robust.\n\nImproved code:"
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.4
)
return self.extract_code(response.choices[0].message.content)
def execute_code(self, code: str, input_data: any) -> any:
"""Execute code safely"""
# Simplified execution
return input_data
# Usage
rsi = RecursiveSelfImprovement(max_iterations=3)
initial_code = """
def process(data):
result = []
for item in data:
result.append(item * 2)
return result
"""
test_suite = [
{"input": [1, 2, 3], "expected": [2, 4, 6]},
{"input": [0], "expected": [0]},
]
result = rsi.improve_recursively(initial_code, test_suite)
print(f"\nFinal improvement: {result['improvement']:.2f}")
Tool Creation and Modification
Dynamic Tool Generation
class ToolCreator:
"""Agent that creates new tools"""
def __init__(self):
self.client = openai.OpenAI()
self.created_tools = {}
def create_tool(self, description: str, examples: List[Dict]) -> Dict:
"""Create new tool from description"""
# Generate tool code
code = self.generate_tool_code(description, examples)
# Generate tool schema
schema = self.generate_tool_schema(description, code)
# Validate
if not self.validate_tool(code):
return {"success": False, "error": "Validation failed"}
# Register tool
tool_name = self.extract_tool_name(code)
self.created_tools[tool_name] = {
"code": code,
"schema": schema,
"description": description
}
return {
"success": True,
"tool_name": tool_name,
"schema": schema
}
def generate_tool_code(self, description: str, examples: List[Dict]) -> str:
"""Generate tool implementation"""
examples_str = "\n".join([
f"Input: {ex['input']}\nOutput: {ex['output']}"
for ex in examples
])
prompt = f"""Create a Python function for this tool:
Description: {description}
Examples:
{examples_str}
Requirements:
1. Function should be self-contained
2. Include type hints
3. Add docstring
4. Handle errors gracefully
5. Return results in consistent format
Code:"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.2
)
return self.extract_code(response.choices[0].message.content)
def generate_tool_schema(self, description: str, code: str) -> Dict:
"""Generate tool schema"""
prompt = f"""Generate a JSON schema for this tool:
Description: {description}
Code:
```python
{code}
Provide schema in OpenAI function calling format: {{ “name”: “tool_name”, “description”: “…”, “parameters”: {{ “type”: “object”, “properties”: {{…}}, “required”: […] }} }}
Schema:“”“
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.1
)
import json
return json.loads(response.choices[0].message.content)
def validate_tool(self, code: str) -> bool:
"""Validate tool code"""
try:
ast.parse(code)
return True
except:
return False
def extract_tool_name(self, code: str) -> str:
"""Extract function name from code"""
tree = ast.parse(code)
for node in ast.walk(tree):
if isinstance(node, ast.FunctionDef):
return node.name
return "unknown_tool"
def modify_tool(self, tool_name: str, modification: str) -> Dict:
"""Modify existing tool"""
if tool_name not in self.created_tools:
return {"success": False, "error": "Tool not found"}
current_code = self.created_tools[tool_name]["code"]
prompt = f"""Modify this tool:
Current code:
{current_code}
Modification: {modification}
Provide modified code:“”“
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.2
)
modified_code = self.extract_code(response.choices[0].message.content)
# Update tool
self.created_tools[tool_name]["code"] = modified_code
return {"success": True, "modified_code": modified_code}
Usage
creator = ToolCreator()
Create new tool
result = creator.create_tool( “Calculate compound interest”, examples=[ {“input”: {“principal”: 1000, “rate”: 0.05, “years”: 3}, “output”: 1157.63}, {“input”: {“principal”: 5000, “rate”: 0.03, “years”: 5}, “output”: 5796.37} ] )
print(f“Created tool: {result[‘tool_name’]}“)
## Abstract Reasoning
### Analogical Reasoning
```python
class AnalogicalReasoner:
"""Agent that reasons by analogy"""
def __init__(self):
self.client = openai.OpenAI()
self.knowledge_base = []
def find_analogies(self, problem: str, domain: str = None) -> List[Dict]:
"""Find analogous problems"""
prompt = f"""Find analogies for this problem:
Problem: {problem}
{f"Domain: {domain}" if domain else ""}
Provide 3 analogous situations from different domains that share similar structure.
For each analogy:
1. Describe the analogous situation
2. Explain the structural similarity
3. Suggest how insights transfer
Analogies:"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.7
)
return self.parse_analogies(response.choices[0].message.content)
def solve_by_analogy(self, problem: str) -> Dict:
"""Solve problem using analogical reasoning"""
# Find analogies
analogies = self.find_analogies(problem)
# Extract solutions from analogies
solutions = []
for analogy in analogies:
solution = self.extract_solution(problem, analogy)
solutions.append(solution)
# Synthesize final solution
final_solution = self.synthesize_solutions(problem, solutions)
return {
"problem": problem,
"analogies": analogies,
"solutions": solutions,
"final_solution": final_solution
}
def extract_solution(self, problem: str, analogy: Dict) -> str:
"""Extract solution approach from analogy"""
prompt = f"""Given this analogy, how would you solve the original problem?
Original problem: {problem}
Analogy: {analogy}
Solution approach:"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.5
)
return response.choices[0].message.content
def synthesize_solutions(self, problem: str, solutions: List[str]) -> str:
"""Synthesize multiple solution approaches"""
solutions_text = "\n\n".join([f"Approach {i+1}:\n{s}" for i, s in enumerate(solutions)])
prompt = f"""Synthesize these solution approaches into one optimal solution:
Problem: {problem}
{solutions_text}
Optimal solution:"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.4
)
return response.choices[0].message.content
def parse_analogies(self, text: str) -> List[Dict]:
"""Parse analogies from text"""
# Simplified parsing
return [{"analogy": text}]
# Usage
reasoner = AnalogicalReasoner()
problem = "How to scale a software system to handle 10x more users?"
result = reasoner.solve_by_analogy(problem)
print(f"Solution: {result['final_solution']}")
Causal Reasoning
class CausalReasoner:
"""Agent that performs causal reasoning"""
def __init__(self):
self.client = openai.OpenAI()
def identify_causal_relationships(self, observations: List[str]) -> Dict:
"""Identify causal relationships"""
obs_text = "\n".join([f"- {obs}" for obs in observations])
prompt = f"""Identify causal relationships in these observations:
{obs_text}
For each relationship:
1. Cause
2. Effect
3. Confidence (low/medium/high)
4. Explanation
Causal relationships:"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.3
)
return self.parse_causal_relationships(response.choices[0].message.content)
def predict_intervention_effect(self,
current_state: str,
intervention: str) -> str:
"""Predict effect of intervention"""
prompt = f"""Predict the causal effect of this intervention:
Current state: {current_state}
Intervention: {intervention}
Analyze:
1. Direct effects
2. Indirect effects
3. Potential unintended consequences
4. Confidence in prediction
Prediction:"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.4
)
return response.choices[0].message.content
def explain_outcome(self, outcome: str, context: str) -> str:
"""Explain why outcome occurred"""
prompt = f"""Explain the causal chain that led to this outcome:
Context: {context}
Outcome: {outcome}
Provide:
1. Root causes
2. Contributing factors
3. Causal chain
4. Alternative explanations
Explanation:"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.4
)
return response.choices[0].message.content
def parse_causal_relationships(self, text: str) -> Dict:
"""Parse causal relationships"""
return {"relationships": text}
# Usage
causal = CausalReasoner()
observations = [
"Website traffic increased by 50%",
"New marketing campaign launched last week",
"Server response time increased",
"User complaints about slow loading"
]
relationships = causal.identify_causal_relationships(observations)
print(f"Causal relationships: {relationships}")
Long-Horizon Planning
Hierarchical Planning
class LongHorizonPlanner:
"""Agent for long-horizon planning"""
def __init__(self):
self.client = openai.OpenAI()
def create_long_term_plan(self,
goal: str,
horizon: str = "1 year",
constraints: List[str] = None) -> Dict:
"""Create long-term hierarchical plan"""
constraints_text = "\n".join(constraints) if constraints else "None"
prompt = f"""Create a detailed long-term plan:
Goal: {goal}
Time horizon: {horizon}
Constraints: {constraints_text}
Create a hierarchical plan with:
1. High-level milestones (quarterly)
2. Medium-level objectives (monthly)
3. Low-level tasks (weekly)
For each level:
- Clear deliverables
- Success criteria
- Dependencies
- Risk factors
Plan:"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.5
)
return self.parse_plan(response.choices[0].message.content)
def adapt_plan(self,
current_plan: Dict,
new_information: str) -> Dict:
"""Adapt plan based on new information"""
prompt = f"""Adapt this plan based on new information:
Current plan: {current_plan}
New information: {new_information}
Provide:
1. What needs to change
2. Updated plan
3. Rationale for changes
4. New risks
Adapted plan:"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.4
)
return self.parse_plan(response.choices[0].message.content)
def evaluate_progress(self,
plan: Dict,
completed_tasks: List[str]) -> Dict:
"""Evaluate progress toward goal"""
prompt = f"""Evaluate progress on this plan:
Plan: {plan}
Completed tasks: {completed_tasks}
Provide:
1. Completion percentage
2. On track / behind / ahead
3. Blockers
4. Recommendations
Evaluation:"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.3
)
return self.parse_evaluation(response.choices[0].message.content)
def parse_plan(self, text: str) -> Dict:
"""Parse plan from text"""
return {"plan": text}
def parse_evaluation(self, text: str) -> Dict:
"""Parse evaluation from text"""
return {"evaluation": text}
# Usage
planner = LongHorizonPlanner()
plan = planner.create_long_term_plan(
goal="Build and launch a successful AI product",
horizon="1 year",
constraints=["Budget: $500K", "Team size: 5 people"]
)
print(f"Plan created: {plan}")
Best Practices
- Safety first: Validate self-modifications
- Incremental improvement: Small, tested changes
- Human oversight: Critical decisions need review
- Rollback capability: Ability to revert changes
- Performance tracking: Monitor improvements
- Ethical boundaries: Respect limitations
- Transparency: Explain reasoning
- Testing: Thorough validation
- Documentation: Track changes
- Research awareness: Stay current
Next Steps
You now understand frontier capabilities! Next, we’ll explore emerging paradigms in agent research.