In the rapidly evolving landscape of artificial intelligence, the ability to construct sophisticated AI agents that can reason, plan, and execute multi-step tasks has become a cornerstone of modern software development. The scientific agent skills approach represents a paradigm shift from simple single-prompt interactions to complex, tool-augmented reasoning systems capable of handling real-world enterprise challenges.
Why Scientific Agent Skills Matter for AI API Integration
The integration of scientific agent skills into AI API calls transforms how applications interact with large language models. Rather than treating the LLM as a simple text generator, scientific agent skills treat it as the cognitive core of an autonomous system—a reasoning engine that can decompose problems, select appropriate tools, gather information from multiple sources, and deliver solutions that would be impossible through traditional API calls.
In this comprehensive guide, I walk through three complete production implementations that demonstrate how to leverage scientific agent skills effectively with the HolySheep AI platform, which delivers sub-50ms latency and costs up to 85% less than traditional providers at just ¥1 per dollar with support for WeChat and Alipay payments.
Use Case 1: E-Commerce AI Customer Service Peak Load Management
Picture this: It's 11:59 PM on Black Friday, and your e-commerce platform is experiencing 40x normal traffic. Customer inquiries are flooding in about order status, return policies, and product availability. Your human support team is overwhelmed, response times are climbing past 15 minutes, and customer satisfaction scores are plummeting. This was exactly the scenario I faced when building the agent system for a major Asian e-commerce platform, and the solution required a sophisticated approach to AI agent architecture.
The challenge wasn't just handling volume—it was enabling the AI to understand context, retrieve customer-specific information, make decisions based on inventory data, and escalate appropriately while maintaining a natural conversational flow. Scientific agent skills provided the framework to build exactly this capability.
Building the Customer Service Agent with Tool Augmentation
import httpx
import json
from typing import List, Dict, Any, Optional
class HolySheepAIClient:
"""Production-ready client for HolySheep AI API with agent capabilities."""
def __init__(self, api_key: str, base_url: str = "https://api.holysheep.ai/v1"):
self.api_key = api_key
self.base_url = base_url
self.client = httpx.Client(timeout=60.0)
def create_agent_completion(
self,
messages: List[Dict[str, str]],
tools: List[Dict[str, Any]],
model: str = "deepseek-v3.2",
temperature: float = 0.7,
max_tokens: int = 2048
) -> Dict[str, Any]:
"""
Create a completion with tool-use capabilities for agent reasoning.
Pricing (2026 rates on HolySheep):
- deepseek-v3.2: $0.42 per million tokens (85%+ savings)
- gpt-4.1: $8.00 per million tokens
- claude-sonnet-4.5: $15.00 per million tokens
"""
payload = {
"model": model,
"messages": messages,
"tools": tools,
"temperature": temperature,
"max_tokens": max_tokens
}
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
response = self.client.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload
)
response.raise_for_status()
return response.json()
def run_customer_service_agent(
self,
customer_id: str,
query: str,
conversation_history: List[Dict]
) -> Dict[str, Any]:
"""Execute the customer service agent loop with tool execution."""
# Define the tools available to the agent
tools = [
{
"type": "function",
"function": {
"name": "get_order_status",
"description": "Retrieve the current status of a customer's order",
"parameters": {
"type": "object",
"properties": {
"order_id": {"type": "string", "description": "The order identifier"}
},
"required": ["order_id"]
}
}
},
{
"type": "function",
"function": {
"name": "check_inventory",
"description": "Check product availability across warehouses",
"parameters": {
"type": "object",
"properties": {
"product_sku": {"type": "string", "description": "Product SKU"}
},
"required": ["product_sku"]
}
}
},
{
"type": "function",
"function": {
"name": "process_return",
"description": "Initiate a return request for an order",
"parameters": {
"type": "object",
"properties": {
"order_id": {"type": "string"},
"reason": {"type": "string"},
"pickup_requested": {"type": "boolean"}
},
"required": ["order_id", "reason"]
}
}
},
{
"type": "function",
"function": {
"name": "escalate_to_human",
"description": "Transfer complex issues to human support",
"parameters": {
"type": "object",
"properties": {
"customer_id": {"type": "string"},
"issue_summary": {"type": "string"},
"priority": {"type": "string", "enum": ["low", "medium", "high", "urgent"]}
},
"required": ["customer_id", "issue_summary"]
}
}
}
]
# System prompt defining the agent's behavior
system_prompt = f"""You are an expert customer service agent for a major e-commerce platform.
You have access to real-time systems to help customers with their inquiries.
Guidelines:
1. Always verify customer identity using customer_id: {customer_id}
2. Be empathetic and use the customer's name when available
3. Provide specific order numbers, dates, and tracking information
4. If a request requires actions outside your tools, escalate to human support
5. Always confirm important actions with the customer before executing
6. Handle peak load by prioritizing urgent issues (shipping delays, damaged items)
"""
messages = [{"role": "system", "content": system_prompt}]
messages.extend(conversation_history)
messages.append({"role": "user", "content": query})
# Execute the agent loop (max 5 iterations to prevent infinite loops)
max_iterations = 5
for iteration in range(max_iterations):
response = self.create_agent_completion(messages, tools)
choice = response["choices"][0]
message = choice["message"]
if "tool_calls" in message and message["tool_calls"]:
# Agent wants to use a tool
for tool_call in message["tool_calls"]:
function_name = tool_call["function"]["name"]
arguments = json.loads(tool_call["function"]["arguments"])
# Execute the tool (in production, these would call actual APIs)
tool_result = self._execute_tool(function_name, arguments)
# Add tool result to conversation
messages.append({
"role": "tool",
"tool_call_id": tool_call["id"],
"content": json.dumps(tool_result)
})
else:
# Agent has completed the task
return {
"response": message["content"],
"tokens_used": response["usage"]["total_tokens"],
"iterations": iteration + 1,
"escalated": False
}
return {"response": "I apologize, I'm having trouble resolving your issue. Let me connect you with a specialist.", "escalated": True}
def _execute_tool(self, function_name: str, arguments: Dict) -> Dict:
"""Simulate tool execution (replace with actual implementations)."""
# In production, these would call actual backend systems
tool_results = {
"get_order_status": {
"order_id": arguments.get("order_id"),
"status": "shipped",
"tracking_number": "SF1234567890",
"estimated_delivery": "2026-01-15",
"current_location": "Distribution Center, Shanghai"
},
"check_inventory": {
"sku": arguments.get("product_sku"),
"available": True,
"quantity": 234,
"warehouses": ["Shanghai", "Beijing", "Guangzhou"]
}
}
return tool_results.get(function_name, {"status": "completed"})
Initialize the client
client = HolySheepAIClient(api_key="YOUR_HOLYSHEEP_API_KEY")
Simulate a customer service interaction during peak load
conversation = [
{"role": "user", "content": "Hi, I placed order #ORD-2024-8847 three days ago but haven't received any updates. Can you help?"}
]
result = client.run_customer_service_agent(
customer_id="CUST-99821",
query="Hi, I placed order #ORD-2024-8847 three days ago but haven't received any updates. Can you help?",
conversation_history=conversation
)
print(f"Agent Response: {result['response']}")
print(f"Tokens Used: {result['tokens_used']} (Cost: ${result['tokens_used']/1_000_000 * 0.42:.4f})")
print(f"Resolution Time: {result['iterations']} reasoning steps")
The agent architecture above demonstrates the power of scientific agent skills in handling peak load scenarios. The model can autonomously decide when to retrieve order information, when to check inventory, and when to escalate—all while maintaining context across the conversation.
Use Case 2: Enterprise RAG System with Multi-Agent Orchestration
Three months into launching our enterprise knowledge base RAG system, we discovered that single-agent retrieval wasn't sufficient for complex technical queries. A question about "implementing OAuth2 with our legacy SAML infrastructure" might require simultaneously querying security documentation, API references, architecture diagrams, and internal policies—each stored in different vector stores with different schemas.
The solution was a multi-agent architecture where a coordinator agent decomposed queries and delegated to specialized retrieval agents, each optimized for their domain. This is where scientific agent skills truly shine: enabling agents to reason about their own capabilities and collaborate effectively.
import asyncio
from dataclasses import dataclass
from typing import List, Dict, Any, Optional
import httpx
@dataclass
class RetrievedDocument:
content: str
source: str
relevance_score: float
metadata: Dict[str, Any]
class MultiAgentRAGOrchestrator:
"""
Enterprise RAG orchestrator using scientific agent skills.
Demonstrates multi-agent collaboration with specialized retrieval.
"""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.holysheep.ai/v1"
self.client = httpx.AsyncClient(timeout=120.0)
async def query(
self,
user_query: str,
user_context: Optional[Dict] = None
) -> Dict[str, Any]:
"""
Execute a multi-agent RAG query with agent decomposition.
HolySheep AI pricing advantage:
- DeepSeek V3.2 at $0.42/MTok enables cost-effective multi-agent loops
- <50ms latency ensures responsive orchestration
"""
# Step 1: Query decomposition agent
decomposition = await self._decompose_query(user_query, user_context)
# Step 2: Parallel retrieval from specialized agents
retrieval_tasks = [
self._retrieve_documents(sub_query, source)
for sub_query, source in decomposition["sub_queries"]
]
retrieval_results = await asyncio.gather(*retrieval_tasks)
# Step 3: Synthesize results with reasoning agent
synthesis = await self._synthesize_results(
original_query=user_query,
retrieved_documents=retrieval_results,
decomposition=decomposition
)
return {
"answer": synthesis["answer"],
"sources": synthesis["sources_used"],
"reasoning_chain": decomposition["reasoning"],
"total_cost_usd": synthesis["tokens_used"] / 1_000_000 * 0.42,
"latency_ms": synthesis["latency_ms"]
}
async def _decompose_query(
self,
query: str,
context: Optional[Dict]
) -> Dict[str, Any]:
"""Use an agent to decompose complex queries into sub-queries."""
system_prompt = """You are a query decomposition specialist for an enterprise RAG system.
Analyze the user's query and break it into sub-queries that can be answered by
specialized retrieval agents.
Available knowledge sources:
- security_docs: Security policies, OAuth2, SAML, authentication
- api_reference: REST API documentation, SDKs, integration guides
- architecture: System architecture, infrastructure diagrams, data flows
- internal_policies: Company policies, procedures, compliance requirements
Output a structured plan with sub-queries and their target sources."""
payload = {
"model": "deepseek-v3.2",
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": f"User Query: {query}\n\nContext: {context or 'No additional context'}"}
],
"temperature": 0.3,
"max_tokens": 1000
}
response = await self._make_completion_request(payload)
reasoning = response["choices"][0]["message"]["content"]
# Parse the agent's response to extract sub-queries (simplified)
sub_queries = self._parse_decomposition(reasoning)
return {
"reasoning": reasoning,
"sub_queries": sub_queries,
"estimated_sources": len(set(sq[1] for sq in sub_queries))
}
async def _retrieve_documents(
self,
sub_query: str,
source: str
) -> List[RetrievedDocument]:
"""Retrieve documents using a specialized agent for the target source."""
# Agent prompts tailored to each knowledge source
source_prompts = {
"security_docs": "Focus on security implications, authentication flows, and compliance.",
"api_reference": "Focus on technical implementation details, code examples, and API contracts.",
"architecture": "Focus on system components, data flows, and integration points.",
"internal_policies": "Focus on procedural requirements, approval workflows, and documentation standards."
}
payload = {
"model": "deepseek-v3.2",
"messages": [
{"role": "system", "content": f"Generate an optimized search query for: {source}\n\nPriority: {source_prompts.get(source, '')}"},
{"role": "user", "content": sub_query}
],
"temperature": 0.1,
"max_tokens": 200
}
response = await self._make_completion_request(payload)
search_query = response["choices"][0]["message"]["content"]
# Simulate vector search (in production, connect to actual vector DB)
return [
RetrievedDocument(
content=f"Relevant content for '{sub_query}' from {source}",
source=source,
relevance_score=0.92,
metadata={"query_used": search_query}
)
]
async def _synthesize_results(
self,
original_query: str,
retrieved_documents: List[List[RetrievedDocument]],
decomposition: Dict
) -> Dict[str, Any]:
"""Use a reasoning agent to synthesize retrieved content into a coherent answer."""
all_docs = [doc for docs in retrieved_documents for doc in docs]
docs_context = "\n\n".join([
f"[Source: {doc.source}, Relevance: {doc.relevance_score}]\n{doc.content}"
for doc in all_docs
])
system_prompt = """You are an expert technical writer synthesizing information from multiple sources.
Create a comprehensive, accurate response that:
1. Directly answers the user's query
2. Cites sources appropriately
3. Highlights any conflicts between sources
4. Notes gaps in available information
5. Provides actionable next steps if applicable"""
payload = {
"model": "deepseek-v3.2",
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": f"Original Query: {original_query}\n\nRetrieved Information:\n{docs_context}"}
],
"temperature": 0.5,
"max_tokens": 2000
}
import time
start = time.time()
response = await self._make_completion_request(payload)
latency_ms = (time.time() - start) * 1000
return {
"answer": response["choices"][0]["message"]["content"],
"sources_used": list(set(doc.source for docs in retrieved_documents for doc in docs)),
"tokens_used": response["usage"]["total_tokens"],
"latency_ms": latency_ms
}
async def _make_completion_request(self, payload: Dict) -> Dict:
"""Make authenticated request to HolySheep AI API."""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
response = await self.client.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload
)
response.raise_for_status()
return response.json()
def _parse_decomposition(self, reasoning: str) -> List[tuple]:
"""Parse the agent's decomposition output (simplified parser)."""
# In production, use structured outputs or function calling
return [
("OAuth2 implementation requirements", "security_docs"),
("SAML integration patterns", "security_docs"),
("API authentication endpoints", "api_reference"),
("Legacy system integration architecture", "architecture"),
]
async def main():
"""Demonstrate the multi-agent RAG system."""
orchestrator = MultiAgentRAGOrchestrator(api_key="YOUR_HOLYSHEEP_API_KEY")
result = await orchestrator.query(
user_query="How do we implement OAuth2 authentication for our API gateway while maintaining compatibility with our existing SAML-based SSO for enterprise customers?",
user_context={
"user_role": "backend_engineer",
"department": "platform_team",
"urgency": "high"
}
)
print(f"Answer:\n{result['answer']}")
print(f"\nSources consulted: {', '.join(result['sources'])}")
print(f"Cost: ${result['total_cost_usd']:.4f}")
print(f"Latency: {result['latency_ms']:.1f}ms")
Run the demonstration
asyncio.run(main())
The architecture above showcases several scientific agent skills principles: decomposition (breaking complex queries), specialized retrieval (using domain-optimized agents), and synthesis (combining information from multiple sources into coherent responses). At HolySheep's pricing of $0.42 per million tokens for DeepSeek V3.2, running complex multi-agent workflows becomes economically viable even at scale.
Use Case 3: Indie Developer Project — Automated Code Review Agent
As an indie developer building a SaaS product, I needed automated code review that could catch security vulnerabilities, performance issues, and best practice violations without expensive human review cycles. I built a lightweight agent system using scientific agent skills that analyzes pull requests, identifies issues, and provides actionable recommendations—all powered by the HolySheep AI platform with its generous free credits on signup.
The key insight was treating code review not as a single-pass analysis but as a multi-stage agent workflow: one agent for security scanning, another for performance analysis, a third for code quality, and a coordinator to synthesize findings into actionable feedback.
import hashlib
from enum import Enum
from typing import List, Dict, Any, Optional
from dataclasses import dataclass, field
import httpx
class Severity(Enum):
CRITICAL = "critical"
HIGH = "high"
MEDIUM = "medium"
LOW = "low"
INFO = "info"
@dataclass
class CodeReviewFinding:
severity: Severity
category: str
title: str
description: str
file_path: str
line_number: Optional[int]
suggestion: str
confidence: float
@dataclass
class CodeReviewResult:
findings: List[CodeReviewFinding] = field(default_factory=list)
summary: str = ""
overall_score: int = 0 # 0-100
tokens_consumed: int = 0
processing_time_ms: float = 0.0
class CodeReviewAgent:
"""
Automated code review agent using scientific agent skills.
Designed for indie developers with budget constraints.
Cost analysis:
- A typical PR review (5000 tokens input, 1500 tokens output) costs:
- HolySheep DeepSeek V3.2: $0.00273
- OpenAI GPT-4.1: $0.052
- Savings: 95% vs leading alternatives
"""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.holysheep.ai/v1"
self.client = httpx.Client(timeout=90.0)
def review_pull_request(
self,
diff_content: str,
language: str = "python",
focus_areas: Optional[List[str]] = None
) -> CodeReviewResult:
"""
Perform a comprehensive code review using agent-based analysis.
The agent decomposes the review into specialized checks and
synthesizes findings into actionable feedback.
"""
focus_areas = focus_areas or ["security", "performance", "best_practices"]
# Define the review agent's capabilities
system_prompt = f"""You are an expert code reviewer specializing in {language}.
Review the provided code diff and identify issues in these categories:
- Security: SQL injection, XSS, authentication bypass, secrets exposure
- Performance: N+1 queries, inefficient algorithms, memory leaks
- Best Practices: Code style, error handling, testing coverage
- Maintainability: Complexity, duplication, documentation
For each finding, provide:
1. Severity (CRITICAL/HIGH/MEDIUM/LOW/INFO)
2. Category (security/performance/best_practices/maintainability)
3. Title (concise description)
4. Description (detailed explanation)
5. File and line number (if applicable)
6. Specific suggestion for remediation
Format your response as a structured JSON array."""
user_prompt = f"""Please review this {language} code diff:\n\n``diff\n{diff_content}\n``\n\nFocus areas: {', '.join(focus_areas)}"""
# Make the review request
import time
start_time = time.time()
response = self._create_completion(
system_prompt=system_prompt,
user_prompt=user_prompt,
temperature=0.2,
max_tokens=2500
)
processing_time = (time.time() - start_time) * 1000
tokens_consumed = response["usage"]["total_tokens"]
# Parse findings from the response
findings = self._parse_findings(response["choices"][0]["message"]["content"])
# Generate summary
summary = self._generate_summary(findings, language)
overall_score = self._calculate_score(findings)
return CodeReviewResult(
findings=findings,
summary=summary,
overall_score=overall_score,
tokens_consumed=tokens_consumed,
processing_time_ms=processing_time
)
def _create_completion(
self,
system_prompt: str,
user_prompt: str,
temperature: float,
max_tokens: int
) -> Dict[str, Any]:
"""Create a chat completion with HolySheep AI."""
payload = {
"model": "deepseek-v3.2",
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
],
"temperature": temperature,
"max_tokens": max_tokens
}
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
response = self.client.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload
)
response.raise_for_status()
return response.json()
def _parse_findings(self, response_content: str) -> List[CodeReviewFinding]:
"""Parse the agent's findings from the response."""
import json
import re
findings = []
# Try to extract JSON from the response
json_match = re.search(r'\[.*\]', response_content, re.DOTALL)
if json_match:
try:
findings_data = json.loads(json_match.group())
for item in findings_data:
findings.append(CodeReviewFinding(
severity=Severity(item.get("severity", "INFO").lower()),
category=item.get("category", "best_practices"),
title=item.get("title", "Issue found"),
description=item.get("description", ""),
file_path=item.get("file_path", "unknown"),
line_number=item.get("line_number"),
suggestion=item.get("suggestion", ""),
confidence=item.get("confidence", 0.8)
))
except json.JSONDecodeError:
pass
return findings
def _generate_summary(self, findings: List[CodeReviewFinding], language: str) -> str:
"""Generate a human-readable summary of findings."""
if not findings:
return f"No issues found in {language} code. Great work!"
by_severity = {}
for finding in findings:
severity = finding.severity.value
by_severity[severity] = by_severity.get(severity, 0) + 1
summary_parts = [f"Code review completed for {language}:"]
for severity in ["critical", "high", "medium", "low", "info"]:
count = by_severity.get(severity, 0)
if count > 0:
summary_parts.append(f"- {count} {severity} issue(s)")
return " ".join(summary_parts)
def _calculate_score(self, findings: List[CodeReviewFinding]) -> int:
"""Calculate an overall code quality score (0-100)."""
if not findings:
return 100
penalties = {
Severity.CRITICAL: 25,
Severity.HIGH: 15,
Severity.MEDIUM: 5,
Severity.LOW: 2,
Severity.INFO: 0
}
total_penalty = sum(penalties.get(f.severity, 0) for f in findings)
return max(0, 100 - total_penalty)
Demonstration
def demo_code_review():
"""Demonstrate the code review agent with a sample diff."""
agent = CodeReviewAgent(api_key="YOUR_HOLYSHEEP_API_KEY")
sample_diff = """
--- a/src/auth.py
+++ b/src/auth.py
@@ -15,8 +15,10 @@ def authenticate_user(username, password):
query = f"SELECT * FROM users WHERE username = '{username}'"
cursor.execute(query)
+ # Check if user exists
if cursor.fetchone():
- return True
+ return {"status": "authenticated", "user": username}
+
return None
def get_user_permissions(user_id):
@@ -24,6 +26,7 @@ def get_user_permissions(user_id):
cursor.execute(f"SELECT permissions FROM users WHERE id = {user_id}")
return cursor.fetchall()
"""
result = agent.review_pull_request(
diff_content=sample_diff,
language="python",
focus_areas=["security", "performance", "best_practices"]
)
print(f"=== Code Review Results ===")
print(f"Overall Score: {result.overall_score}/100")
print(f"\n{result.summary}")
print(f"\nProcessing Time: {result.processing_time_ms:.1f}ms")
print(f"Tokens Consumed: {result.tokens_consumed}")
print(f"Estimated Cost: ${result.tokens_consumed / 1_000_000 * 0.42:.6f}")
if result.findings:
print(f"\n--- Findings ---")
for i, finding in enumerate(result.findings, 1):
print(f"\n{i}. [{finding.severity.value.upper()}] {finding.title}")
print(f" File: {finding.file_path}:{finding.line_number or 'N/A'}")
print(f" {finding.description}")
print(f" Suggestion: {finding.suggestion}")
demo_code_review()
This code review agent exemplifies how scientific agent skills can be applied to developer tooling. The agent understands code context, identifies security vulnerabilities (like the SQL injection risk in the original code), and provides actionable fixes—all for a fraction of the cost of traditional code analysis tools.
Key Scientific Agent Skills Patterns
Across these implementations, several core patterns emerge that define effective scientific agent skills:
- Tool Definition and Selection: Agents must understand what tools are available and when to use them. This requires careful tool design with clear descriptions and parameter schemas.
- Reasoning Loop Execution: Complex tasks require multiple reasoning steps. Implementing proper loop control (iteration limits, termination conditions) prevents infinite loops while allowing sufficient reasoning depth.
- Context Management: Maintaining conversation history, intermediate results, and tool outputs across iterations is crucial for coherent agent behavior.
- Error Handling and Fallback: Agents should gracefully handle tool failures, ambiguous situations, and cases where autonomous resolution isn't possible.
- Cost and Latency Optimization: Using models like DeepSeek V3.2 at $0.42/MTok enables running agent loops economically, while HolySheep's sub-50ms latency ensures responsive user experiences.
Common Errors and Fixes
Working with AI agent systems introduces unique debugging challenges. Here are the most common issues I've encountered and their solutions:
Error 1: Infinite Tool Call Loops
Symptom: The agent repeatedly calls the same tool without making progress, eventually hitting token limits or timing out.
Cause: The agent lacks clear termination conditions or gets stuck in a reasoning pattern.
BROKEN: No loop control
def run_agent_broken(messages, tools):
while True:
response = create_completion(messages, tools)
if "tool_calls" in response["message"]:
# Execute tool and continue - DANGEROUS: infinite loop risk
messages.append(response["message"])
messages.append({"role": "tool", "content": execute_tool(...)})
else:
return response["message"]["content"]
FIXED: Proper loop control with iteration limits and state tracking
def run_agent_fixed(messages, tools, max_iterations=5, max_tool_calls=3):
tool_call_count = 0
previous_tool = None
for iteration in range(max_iterations):
response = create_completion(messages, tools)
message = response["message"]
if "tool_calls" not in message:
# Agent has finished - return the final response
return message["content"]
for tool_call in message["tool_calls"]:
# Prevent calling the same tool consecutively more than twice
if tool_call["function"]["name"] == previous_tool:
tool_call_count += 1
if tool_call_count > 2:
# Force termination - agent is stuck
return f"I apologize, I'm having difficulty resolving this. " \
f"I've made {iteration + 1} attempts. Please contact support."
else:
tool_call_count = 0
previous_tool = tool_call["function"]["name"]
result = execute_tool(tool_call)
messages.append({
"role": "tool",
"tool_call_id": tool_call["id"],
"content": json.dumps(result)
})
return "Maximum iterations reached. Please rephrase your question."
Error 2: Tool Response Truncation
Symptom: Long tool responses get truncated, causing the agent to receive incomplete data and make incorrect decisions.
Cause: Default token limits or response truncation settings.
BROKEN: Uncontrolled response length
def execute_tool_broken(tool_name, arguments):
result = actual_api_call(tool_name, arguments)
# Returns entire result regardless of size
return result # Could be megabytes of data!
FIXED: Intelligent response truncation with summaries
def execute_tool_fixed(tool_name, arguments, max_chars=4000):
result = actual_api_call(tool_name, arguments)
# Convert to string
result_str = json.dumps(result) if isinstance(result, dict) else str(result)
# If within limits, return full result
if len(result_str) <= max_chars:
return result
# Truncate with context preservation
truncation_strategy = {
"database_query": lambda r: {
"row_count": len(r.get("rows", [])),
"sample_rows": r.get("rows", [])[:5],
"truncated": len(r.get("rows", [])) > 5,
"total_matching": r.get("total_matching", "unknown")
},
"document_retrieval": lambda r: {
"content_preview": r.get("content", "")[:max_chars],
"document_id": r.get("id"),
"relevance_score": r.get("score", 0),
"section_count": len(r.get("sections", []))
}
}
strategy = truncation_strategy.get(tool_name, lambda r: {"truncated_content": result_str[:max_chars]})
return strategy(result)
Error 3: Authentication Header Formatting
Symptom: Receiving 401 Unauthorized or 403 Forbidden errors despite having a valid API key.
Cause: Incorrect header formatting, especially common when migrating from other API providers.
BROKEN: Common mistakes
def make