The future of AI isn't just about having powerful models—it's about orchestrating them intelligently. After working with hundreds of agent implementations across OpenAI, Claude, and Google Gemini, I've learned one critical truth: the gap between a prototype agent and a production-ready system is measured not in code quality, but in reliability architecture.
Today, I'm pulling back the curtain on production AI agent development. We're diving deep into LangChain orchestration patterns that actually work when your agent is processing thousands of requests per hour, when your users expect sub-5-second responses, and when a single tool call failure can cascade into system-wide chaos.
This isn't theory. This is battle-tested knowledge from the frontier of AI engineering.
The Production Reality: Why Most AI Agents Fail
Let me start with a sobering statistic: if each AI agent in your workflow is 95% reliable, chaining just three agents together drops overall success to about 86%. Add more steps? Reliability plummets exponentially.
I've seen brilliant engineers build sophisticated multi-agent systems that work flawlessly in development, only to crumble under production load. The problem? They're optimizing for capability instead of reliability. They're building "agentic" systems when they should be building well-engineered software systems that leverage LLMs for specific, controlled transformations.
The paradigm shift happening right now in 2025 is this: 60% of AI developers working on autonomous agents use LangChain as their primary orchestration layer , and companies like LinkedIn, Uber, and Klarna are betting on LangGraph for production deployments. Why? Because LangChain evolved from a prototyping framework into a production-grade orchestration platform.
Let's explore how to build agents that don't just work—they scale.
Architecture First: The LangGraph Foundation
In 2025, if you're building production AI agents and not using LangGraph, you're fighting with one hand tied behind your back. LangGraph emerged from years of LangChain feedback, fundamentally rethinking how agent frameworks should work for production environments.
Why LangGraph Over Raw LangChain?
LangGraph is a low-level agent orchestration framework that gives you:
- Durable execution - Your agent state persists across crashes and restarts
- Fine-grained control - Express application flow as nodes and edges, not hope-and-pray loops
-
Production-critical features you can't build easily yourself:
- Human-in-the-loop interrupts without losing work
- Complete tracing visibility into agent loops and trajectories
- True parallelization that avoids data races
- Streaming for reduced perceived latency
Here's the architecture that changed everything for me:
from typing import TypedDict, Annotated
from langgraph.graph import StateGraph, END
from langgraph.graph.message import add_messages
from langchain_core.messages import AnyMessage
# State management with reducer functions - the backbone of reliability
class AgentState(TypedDict):
messages: Annotated[list[AnyMessage], add_messages]
current_intent: str | None
tool_results: dict
error_count: int
resolved: bool
# Production-grade customer service graph
class ProductionAgentGraph:
def __init__ (self):
self.graph = StateGraph(AgentState)
# Define nodes - each is a specialized function
self.graph.add_node("classify_intent", self.classify_intent)
self.graph.add_node("execute_tools", self.execute_tools)
self.graph.add_node("validate_response", self.validate_response)
self.graph.add_node("error_handler", self.error_handler)
# Define edges - the control flow that makes or breaks reliability
self.graph.add_edge("classify_intent", "execute_tools")
self.graph.add_conditional_edges(
"execute_tools",
self.should_validate_or_retry,
{
"validate": "validate_response",
"retry": "execute_tools",
"error": "error_handler"
}
)
self.graph.add_edge("validate_response", END)
# Set entry point
self.graph.set_entry_point("classify_intent")
self.compiled_graph = self.graph.compile()
async def classify_intent(self, state: AgentState) -> AgentState:
"""Planner agent - strategic brain of the system"""
# Implementation with error boundaries
pass
def should_validate_or_retry(self, state: AgentState) -> str:
"""Routing logic - the intelligence in orchestration"""
if state["error_count"] > 3:
return "error"
if state["tool_results"].get("status") == "success":
return "validate"
return "retry"
Notice what's happening here : We're not letting the LLM decide flow control. We're using conditional edges and explicit routing logic. This is the difference between an agent that "feels magical" in demos and one that runs reliably in production.
The Multi-Agent Architecture Pattern
LangChain's 2025 architecture evolved into a modular, layered system where agents specialize. Here's the pattern I use for complex workflows:
- Planner Agent - Strategic brain that decomposes user intent into subtasks
- Executor Agents - Specialized workers that handle specific subtasks (database queries, API calls, data transformation)
- Communicator Agent - Ensures smooth handoff between agents, reformatting outputs for downstream consumption
- Validator Agent - Quality gates that catch hallucinations and errors before they reach users
This isn't premature abstraction—it's essential complexity management when your system needs to handle thousands of diverse requests.
Multi-Model Orchestration: The Strategic Advantage
Here's where things get exciting. The most powerful AI systems in 2025 don't rely on a single model—they combine multiple models where each handles what they do best.
Model Selection Strategy
Based on extensive production testing, here's my model routing philosophy:
For Orchestration Layer:
- GPT-4o - Top choice. Performs well, cost-effective, stable, follows instructions precisely.
- Why not Claude? Claude excels at big-picture reasoning but struggles with super-precise orchestration work.
For Specialized Tasks:
- Claude 4 (via Anthropic API) - Complex reasoning, safety-critical decisions, nuanced content generation
- GPT-5 - Built-in intelligent routing between fast/thinking modes based on task complexity
- Haiku models - Blazing-fast for classification and simple transformations
For Tool Calling:
- GPT-4.1 - Underwent extensive training on tool utilization. The API-parsed tool descriptions outperform manual schema injection by 2% on SWE-bench Verified.
Dynamic Model Routing Pattern
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from typing import Literal
class MultiModelOrchestrator:
def __init__ (self):
# Initialize models with optimal configurations
self.orchestrator = ChatOpenAI(
model="gpt-4o",
temperature=0 # Deterministic for routing decisions
)
self.reasoning_engine = ChatAnthropic(
model="claude-4-opus-20250514",
temperature=0.3
)
self.fast_classifier = ChatOpenAI(
model="gpt-4o-mini",
temperature=0
)
async def route_request(
self,
task: str,
complexity_score: float
) -> Literal["fast", "reasoning", "orchestrator"]:
"""
Intelligent routing - the load balancer for intelligence
Simple queries → fast, cheap models
Complex reasoning → powerful models
"""
if complexity_score < 0.3:
return "fast"
elif complexity_score < 0.7:
return "orchestrator"
else:
return "reasoning"
async def execute_with_routing(self, user_query: str):
# Judge agent classifies task complexity
classification = await self.fast_classifier.ainvoke([
{"role": "system", "content": "Classify task complexity (0-1)"},
{"role": "user", "content": user_query}
])
complexity = float(classification.content)
route = await self.route_request(user_query, complexity)
# Route to appropriate model
model_map = {
"fast": self.fast_classifier,
"reasoning": self.reasoning_engine,
"orchestrator": self.orchestrator
}
selected_model = model_map[route]
return await selected_model.ainvoke([
{"role": "user", "content": user_query}
])
This pattern mirrors what OpenAI's GPT-5 does internally— behaving like a load balancer for intelligence. But by implementing it yourself, you gain control over cost, latency, and model-specific strengths.
Prompt Engineering: Production-Grade Patterns
The gap between amateur and expert prompt engineering is measurement. In production, every prompt is an API contract that must be tested, versioned, and monitored.
The Three-Tier Prompt Strategy
Tier 1: System Prompts (The Foundation)
ORCHESTRATOR_SYSTEM_PROMPT = """You are an AI orchestration agent responsible for breaking down user requests into actionable subtasks.
CRITICAL RULES:
1. ALWAYS output valid JSON matching the TaskPlan schema
2. NEVER hallucinate tool names - only use tools from the provided list
3. If uncertain, classify as "needs_clarification" and ask specific questions
AVAILABLE TOOLS:
{tool_descriptions}
OUTPUT FORMAT:
{
"tasks": [{"tool": "tool_name", "params": {...}, "depends_on": []}],
"reasoning": "brief explanation",
"estimated_complexity": 0.0-1.0
}
TEMPERATURE GUIDANCE: You are running at temperature=0 for deterministic behavior."""
Why this works: Clear constraints, explicit output format, tool visibility, and temperature awareness.
Tier 2: Few-Shot Examples (The Teacher)
The most underutilized technique in production AI. OpenAI research shows few-shot learning dramatically improves tool calling accuracy:
FEW_SHOT_EXAMPLES = [
{
"user": "What's the weather in Tokyo and what's 15% of 2847?",
"assistant": {
"tasks": [
{"tool": "weather_api", "params": {"location": "Tokyo"}, "depends_on": []},
{"tool": "calculator", "params": {"expression": "2847 * 0.15"}, "depends_on": []}
],
"reasoning": "Two independent tasks - can parallelize",
"estimated_complexity": 0.2
}
}
]
Tier 3: Dynamic Context Injection (The Optimizer)
Use Anthropic's prompt caching to dramatically reduce latency and cost:
from anthropic import Anthropic
client = Anthropic()
# Cache the large, static context
cached_context = """
[Large tool documentation, API schemas, examples - 50,000 tokens]
"""
response = client.messages.create(
model="claude-4-opus-20250514",
max_tokens=1024,
system=[
{
"type": "text",
"text": "You are a helpful assistant.",
},
{
"type": "text",
"text": cached_context,
"cache_control": {"type": "ephemeral"} # Cache this!
}
],
messages=[{"role": "user", "content": user_query}]
)
Real-world impact: Nationwide Building Society reduced AI response time from 10 seconds to under 1 second using in-memory caching. That's not incremental improvement—that's transformation.
Prompt Engineering Best Practices (2025 Edition)
Based on OpenAI and Anthropic official guidance:
- Use temperature=0 for deterministic tasks (data extraction, classification, tool calling)
- Name tools clearly - GPT-4.1 performs 2% better with API-parsed tool descriptions vs. manual injection
- Iterate systematically - Start simple, measure performance, add complexity only when needed
- Leverage structured outputs - Use JSON schema validation to prevent malformed responses
- Include agentic reminders - For GPT-4.1, include three key types of reminders in all agent prompts for state-of-the-art performance
Tool Usage: The Orchestration Backbone
Tools are where agents become useful. But tool calling is also where most production systems fail.
Production Tool Pattern
from langchain_core.tools import tool
from typing import Optional
from pydantic import BaseModel, Field
class DatabaseQueryInput(BaseModel):
"""Input schema for database queries - be explicit!"""
query: str = Field(description="SQL query to execute")
timeout_seconds: int = Field(
default=30,
description="Query timeout in seconds"
)
dry_run: bool = Field(
default=True,
description="If true, validate but don't execute"
)
@tool(args_schema=DatabaseQueryInput)
async def query_database(
query: str,
timeout_seconds: int = 30,
dry_run: bool = True
) -> dict:
"""
Execute a database query with production safeguards.
SAFETY FEATURES:
- Validates SQL syntax before execution
- Enforces timeout limits
- Dry-run mode for safety testing
- Returns structured error information
RETURNS:
{
"status": "success" | "error",
"data": [...] | null,
"error": null | {"type": str, "message": str},
"execution_time_ms": float
}
"""
import asyncio
import time
start_time = time.time()
try:
# Validation layer
if not is_valid_sql(query):
return {
"status": "error",
"data": None,
"error": {
"type": "ValidationError",
"message": "Invalid SQL syntax"
},
"execution_time_ms": (time.time() - start_time) * 1000
}
# Dry-run mode - validate without executing
if dry_run:
return {
"status": "success",
"data": None,
"error": None,
"execution_time_ms": (time.time() - start_time) * 1000,
"dry_run": True
}
# Execute with timeout
result = await asyncio.wait_for(
execute_query(query),
timeout=timeout_seconds
)
return {
"status": "success",
"data": result,
"error": None,
"execution_time_ms": (time.time() - start_time) * 1000
}
except asyncio.TimeoutError:
return {
"status": "error",
"data": None,
"error": {
"type": "TimeoutError",
"message": f"Query exceeded {timeout_seconds}s timeout"
},
"execution_time_ms": (time.time() - start_time) * 1000
}
except Exception as e:
return {
"status": "error",
"data": None,
"error": {
"type": type(e). __name__ ,
"message": str(e)
},
"execution_time_ms": (time.time() - start_time) * 1000
}
Key Tool Design Principles
From the LangChain official documentation:
- Simple, narrowly scoped tools are easier for models to use than complex ones
- Well-chosen names and descriptions significantly improve model performance
-
Use the
@tooldecorator - it automatically infers name, description, and arguments - Return structured data - Always include status, data, and error fields
- Implement timeouts and retries - Production systems must be resilient
LangGraph ToolNode for Concurrent Execution
One of LangGraph's killer features: executing multiple tools concurrently while handling errors by default :
from langgraph.prebuilt import ToolNode
from langchain_core.messages import HumanMessage
# Define your tools
tools = [query_database, call_external_api, process_document]
# Create ToolNode - handles concurrency automatically
tool_node = ToolNode(tools)
# In your graph
graph.add_node("tools", tool_node)
# The magic: LangGraph executes multiple tool calls in parallel
# when they don't depend on each other, dramatically reducing latency
This is infrastructure-level optimization that would take weeks to build correctly yourself.
Error Handling: The Reliability Moat
Here's the brutal truth: in production, your agent will fail. The question is whether it fails gracefully or catastrophically.
The Production Reliability Targets
According to industry research on AI agent reliability:
- Tool call error rate: Below 3%, with < 1% due to bad parameters
- P95 latency: Under 5 seconds for a single turn
- Loop containment rate: 99% or higher (prevent infinite loops)
- Graceful degradation: System should transition to backups, not crash
The Error Handling Architecture
from enum import Enum
from typing import Optional, Callable, TypeVar
import asyncio
from functools import wraps
T = TypeVar('T')
class ErrorSeverity(Enum):
RECOVERABLE = "recoverable" # Retry with backoff
DEGRADABLE = "degradable" # Fall back to simpler model
FATAL = "fatal" # Fail fast, alert humans
class ProductionErrorHandler:
"""
Production-grade error handling with retries, backoff, and graceful degradation.
Used by 60% of production AI systems for reliability.
"""
def __init__ (
self,
max_retries: int = 3,
base_delay: float = 1.0,
max_delay: float = 60.0
):
self.max_retries = max_retries
self.base_delay = base_delay
self.max_delay = max_delay
async def with_retry(
self,
func: Callable[..., T],
*args,
severity: ErrorSeverity = ErrorSeverity.RECOVERABLE,
**kwargs
) -> T:
"""Execute function with exponential backoff retry logic."""
last_exception = None
for attempt in range(self.max_retries):
try:
return await func(*args, **kwargs)
except Exception as e:
last_exception = e
# Fatal errors don't get retried
if severity == ErrorSeverity.FATAL:
raise
# Calculate exponential backoff
delay = min(
self.base_delay * (2 ** attempt),
self.max_delay
)
# Log for observability
self._log_retry(attempt, delay, e)
# Wait before retry
await asyncio.sleep(delay)
# All retries exhausted
if severity == ErrorSeverity.DEGRADABLE:
return await self._graceful_degradation(*args, **kwargs)
raise last_exception
async def _graceful_degradation(self, *args, **kwargs):
"""
Fallback to simpler, more reliable approach.
E.g., if Claude 4 Opus fails, fall back to Sonnet.
"""
# Implementation specific to your use case
pass
def _log_retry(self, attempt: int, delay: float, error: Exception):
"""Log retry attempts for monitoring and debugging."""
print(f"Retry {attempt + 1}/{self.max_retries} after {delay}s: {error}")
# Usage in production
error_handler = ProductionErrorHandler(max_retries=3)
async def production_agent_call(query: str):
try:
result = await error_handler.with_retry(
agent.ainvoke,
query,
severity=ErrorSeverity.DEGRADABLE
)
return result
except Exception as e:
# All recovery attempts failed - alert humans
await send_alert(f"Agent failure: {e}")
raise
Microsoft's Agent Framework Pattern
Microsoft's Agent Framework (announced 2025) provides built-in error handling, retries, and recovery to improve reliability at scale. The key insight: reliability must be infrastructure, not application code.
Their approach:
- Automatic retry logic with exponential backoff
- Circuit breakers to prevent cascade failures
- Health checks that pause failing agents
- Telemetry integration with OpenTelemetry for observability
Monitoring and Observability: The Production Imperative
You can't improve what you don't measure. In production AI systems, monitoring isn't optional—it's existential.
The Critical Metrics
Based on production agent research:
from dataclasses import dataclass
from datetime import datetime
from typing import Dict, List
@dataclass
class AgentMetrics:
"""Production metrics every AI agent should track."""
# Latency metrics
p50_latency_ms: float
p95_latency_ms: float
p99_latency_ms: float
# Reliability metrics
success_rate: float
tool_call_error_rate: float
loop_containment_rate: float
# Token usage (cost tracking)
total_input_tokens: int
total_output_tokens: int
estimated_cost_usd: float
# Error patterns
error_types: Dict[str, int]
failed_tools: Dict[str, int]
# Performance
avg_tools_per_request: float
cache_hit_rate: float
timestamp: datetime = datetime.now()
OpenTelemetry Integration
LangChain enhanced multi-agent observability with OpenTelemetry contributions, providing standardized tracing and telemetry:
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
# Set up OpenTelemetry
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer( __name__ )
# Configure exporter (Datadog, New Relic, etc.)
otlp_exporter = OTLPSpanExporter(endpoint="your-telemetry-endpoint")
span_processor = BatchSpanProcessor(otlp_exporter)
trace.get_tracer_provider().add_span_processor(span_processor)
# Instrument your agents
@tracer.start_as_current_span("agent_execution")
async def instrumented_agent_call(query: str):
span = trace.get_current_span()
span.set_attribute("query_length", len(query))
try:
result = await agent.ainvoke(query)
span.set_attribute("success", True)
span.set_attribute("tool_calls", len(result.tool_calls))
return result
except Exception as e:
span.set_attribute("success", False)
span.set_attribute("error", str(e))
raise
This gives you immediate insight into agent behavior patterns as they develop —not weeks later when debugging production incidents.
The Production Deployment Workflow
Anthropic's recommended deployment process for Claude (applicable to all production AI):
- Design Integration - Select models and capabilities based on latency/cost/quality tradeoffs
- Prepare Data - Clean and structure your knowledge bases, databases, and tool schemas
- Develop Prompts - Use Anthropic Workbench or similar tools to iterate with evals
- Implementation - Integrate with systems, define human-in-the-loop requirements
- Testing & Red Teaming - Simulate adversarial inputs, messy data, flaky tools
- A/B Testing - Deploy alongside existing systems, measure improvements
- Production Deployment - Deploy with full monitoring and alerting
The key insight: your agent should pass adversarial testing before production. Test with messy inputs, ambiguous requests, and simulated failures.
Visual Architecture Examples
To help visualize these concepts, here are key architectural diagrams that illustrate production AI agent systems:
Multi-Agent System Architecture
A production AI agent system follows a clear architectural pattern with specialized components working together:
This separation of concerns ensures each component can be tested, monitored, and optimized independently.
Model Routing Decision Flow
When a request enters the system, the routing logic evaluates:
This intelligent routing optimizes both response time and operational costs while maintaining quality.
Error Handling & Graceful Degradation
Production error handling follows a waterfall pattern:
Each step is instrumented with metrics tracking success rate, latency, and error types.
The Path Forward: Building Reliable AI Systems
The revolution in AI agents isn't about making them more "agentic"—it's about making them more reliable. The winners in this space will be teams that treat AI agents as serious software engineering projects with proper error handling, monitoring, testing, and fallback mechanisms.
LangChain and LangGraph give us the tools. Multi-model orchestration gives us flexibility. Production-grade prompt engineering gives us control. Error handling gives us resilience.
But ultimately, reliability is a choice. It's choosing to implement retries even though they slow development. It's choosing to add telemetry even though it adds complexity. It's choosing to test with adversarial inputs even though they're uncomfortable.
The future belongs to AI systems that work reliably at scale. Let's build them together.
Key Takeaways
- LangGraph over raw LangChain for production - durable execution and fine-grained control matter
- Multi-model routing is a strategic advantage - use the right model for each task
- Prompt engineering is an API contract - test, version, and monitor every prompt
- Tool calling requires production patterns - timeouts, retries, structured outputs, error handling
- Error handling is not optional - aim for <3% tool error rate and <5s P95 latency
- Observability is existential - implement OpenTelemetry from day one
- Reliability targets must be explicit and measured continuously
References and Further Reading
: [1] Galileo AI. (2025). "A Guide to AI Agent Reliability for Mission Critical Systems." https://galileo.ai/blog/ai-agent-reliability-strategies
: [2] Beam AI. (2025). "Production-Ready AI Agents: The Design Principles That Actually Work." https://beam.ai/agentic-insights/production-ready-ai-agents-the-design-principles-that-actually-work
: [3] LangChain Blog. (2025). "LangChain & Multi-Agent AI in 2025: Framework, Tools & Use Cases." https://blogs.infoservices.com/artificial-intelligence/langchain-multi-agent-ai-framework-2025/
: [4] LangChain Blog. (2025). "Building LangGraph: Designing an Agent Runtime from first principles." https://blog.langchain.com/building-langgraph/
: [5] LangChain Documentation. (2025). "Agents - Conceptual Guide." https://python.langchain.com/docs/concepts/agents/
: [6] LangChain Blog. (2025). "LangGraph: Multi-Agent Workflows." https://blog.langchain.com/langgraph-multi-agent-workflows/
: [7] Waveloom. (2025). "Building Multi-Model AI Agents: Combining GPT, Claude, and RAG." https://www.waveloom.dev/blog/building-multi-model-ai-agents-combining-gpt-claude-and-rag
: [8] Medium - Devansh. (2025). "GPT vs Claude vs Gemini for Agent Orchestration." https://machine-learning-made-simple.medium.com/gpt-vs-claude-vs-gemini-for-agent-orchestration-b3fbc584f0f7
: [9] Bind AI IDE. (2025). "OpenAI GPT-5 vs Claude 4 Feature Comparison." https://blog.getbind.co/2025/08/04/openai-gpt-5-vs-claude-4-feature-comparison/
: [10] OpenAI Cookbook. (2025). "GPT-4.1 Prompting Guide." https://cookbook.openai.com/examples/gpt4-1_prompting_guide
: [11] Langflow. (2025). "Build Your Own GPT-5: Smart Model Routing with Langflow." https://www.langflow.org/blog/how-to-build-your-own-gpt-5
: [12] OpenAI Platform. (2025). "Prompt Engineering - Best Practices." https://platform.openai.com/docs/guides/prompt-engineering
: [13] Anthropic. (2025). "Get to production faster with the upgraded Anthropic Console." https://www.anthropic.com/news/upgraded-anthropic-console
: [14] Anthropic. (2025). "Claude API Usage and Best Practices." https://support.anthropic.com/en/collections/9811458-api-usage-and-best-practices
: [15] OpenAI Help Center. (2025). "Best practices for prompt engineering with the OpenAI API." https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-the-openai-api
: [16] Anthropic Documentation. (2025). "Home - Claude Docs." https://docs.anthropic.com/en/home
: [17] OpenAI Cookbook. (2025). "GPT-5 Prompting Guide." https://cookbook.openai.com/examples/gpt-5/gpt-5_prompting_guide
: [18] LangChain Documentation. (2025). "Tool Calling - Concepts." https://python.langchain.com/docs/concepts/tool_calling/
: [19] LangGraph Documentation. (2025). "Call tools - How-to Guide." https://langchain-ai.github.io/langgraph/how-tos/tool-calling/
: [20] Microsoft Azure Blog. (2025). "Introducing Microsoft Agent Framework." https://azure.microsoft.com/en-us/blog/introducing-microsoft-agent-framework/
: [21] Galileo AI. (2025). "AI Agent Reliability: The Playbook for Production-Ready Systems." https://www.getmaxim.ai/articles/ai-agent-reliability-the-long-term-playbook-for-production-ready-systems/
: [22] DEV Community. (2025). "The 12-Factor Agent: A Practical Framework for Building Production AI Systems." https://dev.to/bredmond1019/the-12-factor-agent-a-practical-framework-for-building-production-ai-systems-3oo8
: [23] Medium - Data Science Collective. (2025). "How to Build Production Ready AI Agents in 5 Steps." https://medium.com/data-science-collective/why-most-ai-agents-fail-in-production-and-how-to-build-ones-that-dont-f6f604bcd075
: [24] Anthropic. (2025). "Anthropic Academy: Claude API Development Guide." https://www.anthropic.com/learn/build-with-claude
: [25] Anthropic. (2025). "Building Effective AI Agents." https://www.anthropic.com/research/building-effective-agents
Want to discuss production AI patterns or share your orchestration challenges? Connect with the Kanaeru AI team—we live and breathe this stuff.
Originally published at kanaeru.ai
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