Stop Building Broken AI Agents! Use awesome-agentic-patterns Instead

You spent six months building an AI agent. It worked beautifully in your Jupyter notebook. Then you deployed it to production—and watched it hallucinate, loop infinitely, burn through your API budget, and silently fail on edge cases you never anticipated. Sound familiar? You're not alone. The gap between demo-grade AI agents and production-ready autonomous systems is killing projects across the industry.

Here's the brutal truth: tutorials show toy demos. Real products hide the messy bits. That slick Twitter demo of an agent coding an entire app? It conveniently omits the circuit breakers, the feedback loops, the memory management, the security sandboxes, and the eval harnesses that make it actually reliable. Developers are reinventing these wheels in isolation, bleeding time and money while repeating the same failures.

But what if you could skip the painful discovery phase? What if the collective battle scars of teams actually shipping AI agents were distilled into repeatable, traceable, production-hardened patterns? Enter awesome-agentic-patterns—a curated catalogue of agentic AI patterns that bridges the gap between "looks cool on Twitter" and "runs reliably at 3 AM without waking the on-call engineer." This isn't another hype repository. It's a field manual forged from real production systems, and it might just save your next agent project from becoming another statistic.

What is awesome-agentic-patterns?

awesome-agentic-patterns is a meticulously curated open-source catalogue of agentic AI patterns—real-world workflows, architectural tricks, and mini-architectures that help autonomous or semi-autonomous AI agents get useful work done in production environments. Created by Nibzard and born from the hard-won lessons documented in the seminal write-up "What Sourcegraph learned building AI coding agents" (May 2025), this repository represents a fundamental shift in how we approach agent development.

The project's philosophy is deceptively simple yet profoundly different from typical AI resources: patterns must be repeatable (used by more than one team), agent-centric (directly improving how agents sense, reason, or act), and traceable (backed by public references like blog posts, talks, repos, or papers). This rigorous filtering is why the repository has gained explosive traction—developers are exhausted by theoretical frameworks and hungry for battle-tested solutions.

What makes awesome-agentic-patterns genuinely special is its living, evolving nature. The repository auto-generates its documentation from a patterns/ folder, ensuring the catalogue stays current as the community contributes new discoveries. Beyond the GitHub repo, the project maintains a sophisticated companion website at agentic-patterns.com built with Astro and deployed on Vercel, featuring interactive exploration tools that transform pattern discovery from tedious reading into strategic decision-making.

The timing couldn't be more critical. As we enter 2025-2026, AI agents are transitioning from experimental toys to core infrastructure. Companies are betting millions on autonomous systems that can code, research, operate infrastructure, and make decisions. Yet the failure rate remains staggeringly high because teams lack pattern literacy—the ability to recognize recurring problems and apply proven solutions. awesome-agentic-patterns fills this gap with over 170 documented patterns across eight critical categories, making it arguably the most comprehensive public resource for production agent architecture available today.

Key Features That Separate Pros from Amateurs

The depth and organization of awesome-agentic-patterns reveals a sophistication that immediately distinguishes serious agent engineering from hobbyist experimentation. Here's what makes this catalogue indispensable:

Eight Production-Critical Categories — The repository organizes patterns into Context & Memory, Feedback Loops, Learning & Adaptation, Orchestration & Control, Reliability & Eval, Security & Safety, Tool Use & Environment, and UX & Collaboration. This taxonomy alone teaches you what dimensions matter in production. Most developers obsess over model selection; this catalogue forces you to confront the full operational surface area of agent systems.

Interactive Web Platform with Decision Tools — The companion website isn't marketing fluff. It offers a Pattern Explorer with filtering by category, status, and complexity; a Compare Tool for side-by-side pattern analysis; a Decision Explorer that guides you to the right pattern for your specific use case; and even Graph Visualization showing pattern relationships. These tools transform pattern selection from intuition-based guessing into structured engineering decisions.

Auto-Generated Living Documentation — The README tables auto-generate from the patterns/ folder via bun run build:data. This means the documentation is never stale, contributions are frictionless, and the community can verify patterns through the same pipeline that publishes them. For teams building their own internal pattern libraries, this architecture is itself a pattern worth studying.

Machine-Readable Pattern Metadata via llms.txt — Perhaps most forward-thinking is the inclusion of llms.txt, a machine-readable documentation file designed specifically for AI assistants and LLMs. This isn't meta-commentary—it's practical infrastructure for the emerging ecosystem of AI-powered development tools. RAG systems can index this catalogue; coding assistants can recommend patterns; LLM-powered tools can make contextually appropriate pattern suggestions. The repository is literally built to be consumed by the next generation of agents.

Rigorous Contribution Standards — The repository explicitly rejects "product announcements or promotions, even if technically valid." This pattern-first discipline ensures quality and prevents the catalogue from becoming another marketing channel masquerading as open source. The bar for inclusion—repeatable, agent-centric, traceable—keeps the signal-to-noise ratio exceptionally high.

Use Cases: Where These Patterns Save Projects

The true value of awesome-agentic-patterns emerges when you map specific patterns to real production scenarios. Here are four concrete situations where this catalogue transforms potential disasters into manageable engineering problems:

1. Autonomous Coding Agents That Don't Destroy Codebases The "Coding Agent CI Feedback Loop" pattern, combined with "Background Agent with CI Feedback" and "Self-Critique Evaluator Loop," creates a robust development assistant that actually improves with use. Instead of agents that generate plausible-looking but broken code, you get systems that compile, test, reflect on failures, and iterate. The "Spec-As-Test Feedback Loop" ensures the agent's output is continuously validated against executable specifications. Teams at Sourcegraph and others have used these patterns to build agents that handle real refactoring tasks without human micromanagement.

2. Long-Running Research and Analysis Pipelines When agents need to work for hours or days on complex tasks, the "Planner-Worker Separation for Long-Running Agents" pattern becomes essential. Combined with "Episodic Memory Retrieval & Injection" and "Progressive Autonomy with Model Evolution," you can build systems that maintain coherence across extended operations, delegate to specialized sub-agents, and escalate to more capable (expensive) models only when justified. The "Agent-Driven Research" pattern provides the architectural backbone for systems that can genuinely explore problem spaces rather than following rigid scripts.

3. Multi-Agent Systems That Coordinate Without Chaos The "Declarative Multi-Agent Topology Definition," "Cross-Cycle Consensus Relay," and "Economic Value Signaling in Multi-Agent Networks" patterns address one of the hardest problems in agent engineering: getting multiple autonomous systems to cooperate productively. These aren't theoretical constructs—they're derived from systems where agents bid for resources, verify each other's outputs, and maintain shared state without central coordination bottlenecks. The "Swarm Migration Pattern" even handles graceful agent replacement and load balancing.

4. Secure Agent Operations in Adversarial Environments Security patterns like "Isolated VM per RL Rollout," "PII Tokenization," "Zero-Trust Agent Mesh," and "Hook-Based Safety Guard Rails for Autonomous Code Agents" aren't paranoia—they're prerequisites for any agent handling sensitive data or executing in production environments. The "Denial Tracking & Permission Escalation" pattern provides audit trails for agent decision-making, while "Sandboxed Tool Authorization" ensures agents can only access capabilities explicitly granted. These patterns transform "hope it doesn't go rogue" into verifiable security postures.

Step-by-Step Installation & Setup Guide

Getting started with awesome-agentic-patterns is intentionally lightweight—the value is in the patterns, not complex tooling. However, maximizing the repository's utility requires understanding its architecture and contribution workflow.

Basic Repository Setup

# Clone the repository
git clone https://github.com/nibzard/awesome-agentic-patterns.git
cd awesome-agentic-patterns

# Install dependencies (requires Bun)
curl -fsSL https://bun.sh/install | bash
bun install

# Generate the auto-generated README sections and site data
bun run build:data

The bun run build:data command is crucial—it regenerates the category tables and pattern listings from the patterns/ folder, ensuring your local copy reflects the latest contributions.

Website Development Environment

For teams wanting to extend the web platform or contribute UI improvements:

# Navigate to the web application
cd apps/web

# Install dependencies
bun install

# Start development server
bun run dev

# Build for production
bun run build

The website is built with Astro, a modern static site generator optimized for content-heavy sites, and deployed on Vercel. The source code in apps/web/ demonstrates production patterns for documentation sites: server-side rendering for SEO, island architecture for interactivity, and edge deployment for global performance.

Pattern Contribution Workflow

To contribute a new pattern—perhaps one you've validated in production:

# Create a feature branch
git checkout -b add-my-pattern

# Create your pattern file following the template in patterns/
# Key requirements from CONTRIBUTING.md:
# - Must be repeatable (multiple teams using it)
# - Must be agent-centric (improves sensing, reasoning, or acting)
# - Must be traceable (public reference: blog, talk, repo, or paper)

# Add your pattern file
touch patterns/my-production-pattern.md

# Regenerate documentation
bun run build:data

# Verify changes and commit
git add .
git commit -m "Add: my-production-pattern"
git push origin add-my-pattern

# Open a PR titled "Add: my-production-pattern"

Consuming Patterns in Your Projects

The most common integration pattern is selective adoption—identifying relevant patterns and implementing them in your agent framework:

# Option 1: Reference patterns directly from the website
# Visit https://agentic-patterns.com and use the Decision Explorer

# Option 2: Index patterns for RAG in your development environment
# Download llms.txt for machine-readable pattern metadata
curl -o agentic-patterns-llms.txt https://agentic-patterns.com/llms.txt

# Option 3: Submodule for version-locked reference
git submodule add https://github.com/nibzard/awesome-agentic-patterns.git docs/agentic-patterns

REAL Code Examples and Pattern Implementations

While awesome-agentic-patterns is primarily a knowledge catalogue rather than a code library, the pattern descriptions and architectural guidance translate directly into implementation. Here are concrete examples derived from the repository's documented patterns, showing how to apply these concepts in production code.

Example 1: Context Window Auto-Compaction Pattern

One of the most common production failures is context window overflow—agents that accumulate conversation history until they exceed token limits and either fail silently or truncate critical information. The "Context Window Auto-Compaction" pattern solves this proactively:

import tiktoken
from typing import List, Dict, Callable

class CompactingContextWindow:
    """
    Implements the Context Window Auto-Compaction pattern from
    awesome-agentic-patterns. Maintains a working window by
    progressively summarizing older interactions.
    """
    
    def __init__(
        self,
        model_name: str = "gpt-4",
        max_tokens: int = 6000,  # Leave headroom below 8k limit
        summarizer: Callable[[List[Dict]], str] = None
    ):
        self.encoder = tiktoken.encoding_for_model(model_name)
        self.max_tokens = max_tokens
        self.interactions: List[Dict] = []  # Raw interactions
        self.compacted_summary: str = ""     # Progressive summary
        self.summarizer = summarizer or self._default_summarizer
    
    def _count_tokens(self, text: str) -> int:
        return len(self.encoder.encode(text))
    
    def _default_summarizer(self, interactions: List[Dict]) -> str:
        """Fallback: extract key facts and decisions."""
        # In production, replace with LLM call or structured extraction
        key_points = []
        for turn in interactions:
            if turn.get("critical_decision"):
                key_points.append(f"Decision: {turn['critical_decision']}")
        return "; ".join(key_points) if key_points else "[Prior context summarized]"
    
    def add_interaction(self, role: str, content: str, metadata: Dict = None):
        """Add new interaction, compacting older context if needed."""
        new_interaction = {
            "role": role,
            "content": content,
            "metadata": metadata or {},
            "timestamp": time.time()
        }
        
        # Proactive compaction: check before adding
        projected = self._project_token_count(new_interaction)
        while projected > self.max_tokens and len(self.interactions) > 2:
            # Compact oldest batch into summary
            batch_size = max(1, len(self.interactions) // 4)
            old_batch = self.interactions[:batch_size]
            self.interactions = self.interactions[batch_size:]
            
            # Merge into progressive summary
            new_summary = self.summarizer(old_batch)
            self.compacted_summary = f"{self.compacted_summary}\n{new_summary}".strip()
            
            projected = self._project_token_count(new_interaction)
        
        self.interactions.append(new_interaction)
    
    def _project_token_count(self, new_interaction: Dict) -> int:
        """Calculate total tokens if new interaction were added."""
        total = self._count_tokens(self.compacted_summary)
        for turn in self.interactions:
            total += self._count_tokens(turn["content"])
        total += self._count_tokens(new_interaction["content"])
        return total
    
    def get_messages(self) -> List[Dict]:
        """Return formatted messages for LLM API call."""
        messages = []
        if self.compacted_summary:
            messages.append({
                "role": "system",
                "content": f"Prior context summary: {self.compacted_summary}"
            })
        messages.extend([
            {"role": t["role"], "content": t["content"]}
            for t in self.interactions
        ])
        return messages

This implementation demonstrates the pattern's core insight: compaction should be progressive and loss-bounded, not an emergency truncation that discards arbitrarily. The summarizer injection point allows teams to customize based on their domain—code agents might preserve function signatures and type information, while research agents might keep citation chains and methodological decisions.

Example 2: Circuit Breaker Pattern for Agent Tool Calls

The "Agent Circuit Breaker" pattern prevents cascading failures when external tools degrade or fail:

import time
from enum import Enum, auto
from dataclasses import dataclass
from typing import Callable, Any, Optional

class CircuitState(Enum):
    CLOSED = auto()      # Normal operation
    OPEN = auto()        # Failing, reject fast
    HALF_OPEN = auto()   # Testing if recovered

@dataclass
class CircuitBreakerConfig:
    """Configuration for the Agent Circuit Breaker pattern."""
    failure_threshold: int = 5       # Failures before opening
    recovery_timeout: float = 30.0   # Seconds before half-open test
    half_open_max_calls: int = 3     # Test calls in half-open state
    success_threshold: int = 2       # Successes to close circuit

class AgentCircuitBreaker:
    """
    Prevents agent tool call cascades by failing fast when
    dependencies are unhealthy. From awesome-agentic-patterns
    Reliability & Eval category.
    """
    
    def __init__(self, config: CircuitBreakerConfig = None):
        self.config = config or CircuitBreakerConfig()
        self.state = CircuitState.CLOSED
        self.failures = 0
        self.successes = 0
        self.last_failure_time: Optional[float] = None
        self.half_open_calls = 0
        self._lock = threading.RLock()
    
    def call(self, operation: Callable, *args, **kwargs) -> Any:
        """Execute operation with circuit breaker protection."""
        with self._lock:
            if self.state == CircuitState.OPEN:
                if time.time() - self.last_failure_time > self.config.recovery_timeout:
                    self.state = CircuitState.HALF_OPEN
                    self.half_open_calls = 0
                    self.successes = 0
                else:
                    raise CircuitBreakerOpenError(
                        "Tool temporarily unavailable; agent should degrade gracefully"
                    )
            
            if self.state == CircuitState.HALF_OPEN:
                if self.half_open_calls >= self.config.half_open_max_calls:
                    raise CircuitBreakerOpenError("Half-open quota exceeded")
                self.half_open_calls += 1
        
        # Execute outside lock to prevent blocking
        try:
            result = operation(*args, **kwargs)
            self._on_success()
            return result
        except Exception as e:
            self._on_failure()
            raise
    
    def _on_success(self):
        with self._lock:
            if self.state == CircuitState.HALF_OPEN:
                self.successes += 1
                if self.successes >= self.config.success_threshold:
                    self.state = CircuitState.CLOSED
                    self.failures = 0
            else:
                self.failures = max(0, self.failures - 1)
    
    def _on_failure(self):
        with self._lock:
            self.failures += 1
            self.last_failure_time = time.time()
            
            if self.state == CircuitState.HALF_OPEN:
                self.state = CircuitState.OPEN
            elif self.failures >= self.config.failure_threshold:
                self.state = CircuitState.OPEN

class CircuitBreakerOpenError(Exception):
    """Raised when circuit is open; agent should handle gracefully."""
    pass

The critical agent-specific adaptation here is the graceful degradation instruction in the error message. Unlike microservice circuit breakers that simply fail, agent circuit breakers should trigger fallback behaviors—switching to alternative tools, reducing ambition, or escalating to human review.

Example 3: Plan-Then-Execute with Structured Output

The "Plan-Then-Execute Pattern" combined with "Structured Output Specification" creates predictable, debuggable agent behavior:

from pydantic import BaseModel, Field
from typing import List, Literal, Optional
import json

class ExecutionStep(BaseModel):
    """Single step in an agent's execution plan."""
    step_number: int = Field(..., description="Sequential order")
    action_type: Literal["tool_call", "reasoning", "human_ask", "terminate"]
    tool_name: Optional[str] = Field(None, description="Tool to invoke if action_type=tool_call")
    parameters: dict = Field(default_factory=dict, description="Tool parameters")
    expected_output: str = Field(..., description="What this step should produce")
    rollback_procedure: str = Field(..., description="How to undo if this step fails")

class AgentPlan(BaseModel):
    """
    Structured plan output forcing the agent to think before acting.
    Implements Plan-Then-Execute + Structured Output Specification patterns.
    """
    goal: str = Field(..., description="Original user request")
    context_summary: str = Field(..., description="Relevant context from memory")
    risk_assessment: Literal["low", "medium", "high", "critical"]
    steps: List[ExecutionStep] = Field(..., min_items=1, max_items=20)
    success_criteria: List[str] = Field(..., min_items=1)
    
    def validate_plan(self) -> bool:
        """Pre-flight validation before execution begins."""
        if self.risk_assessment == "critical" and len(self.steps) > 5:
            raise ValueError("Critical-risk plans must be decomposed into smaller batches")
        
        # Ensure no destructive operations without rollback
        for step in self.steps:
            if "delete" in step.tool_name or "write" in step.tool_name:
                if not step.rollback_procedure or step.rollback_procedure == "none":
                    raise ValueError(f"Step {step.step_number} lacks rollback procedure")
        
        return True

def generate_plan(user_request: str, context: str, planner_llm) -> AgentPlan:
    """
    Force structured planning before any tool execution.
    The LLM must output valid AgentPlan JSON, not free text.
    """
    system_prompt = """You are a planning agent. Given a user request and context,
    output a structured execution plan as valid JSON matching the AgentPlan schema.
    
    Rules:
    - Every tool call must have a rollback_procedure
    - Risk assess honestly: prefer medium over low if uncertain
    - Decompose complex goals into verifiable steps
    - Include explicit success_criteria for verification
    """
    
    response = planner_llm.complete(
        system=system_prompt,
        user=f"Request: {user_request}\n\nContext: {context}",
        response_format={"type": "json_object"}  # OpenAI structured output
    )
    
    plan = AgentPlan.model_validate_json(response.content)
    plan.validate_plan()  # Schema validation + custom business rules
    return plan

# Execution with monitoring
async def execute_plan(plan: AgentPlan, executor_agent, observer: Callable):
    """Execute validated plan with progress tracking and rollback capability."""
    completed_steps = []
    
    for step in plan.steps:
        # Pre-execution observation hook
        observer("step_start", step)
        
        try:
            if step.action_type == "tool_call":
                result = await executor_agent.call_tool(
                    step.tool_name, 
                    step.parameters
                )
            elif step.action_type == "reasoning":
                result = await executor_agent.reason(step.expected_output)
            # ... handle other action types
            
            completed_steps.append((step, result))
            observer("step_success", step, result)
            
        except Exception as e:
            observer("step_failure", step, e)
            # Trigger rollback for completed steps if configured
            if plan.risk_assessment in ["high", "critical"]:
                await rollback_steps(completed_steps[::-1])
            raise
    
    # Final verification against success criteria
    verification = await verify_success(plan.success_criteria, completed_steps)
    if not verification.all_met:
        raise PlanVerificationError(f"Unmet criteria: {verification.failures}")
    
    return completed_steps

This combined implementation shows how patterns reinforce each other: structured output forces explicit planning, which enables reliable execution, which supports meaningful verification. The observer callback implements the "LLM Observability" pattern, while rollback_steps embodies "Self-Healing Retries" from the Feedback Loops category.

Advanced Usage & Best Practices

Mastering awesome-agentic-patterns requires moving beyond individual pattern adoption to pattern composition and strategic selection. Here are pro tips from production deployments:

Compose Patterns in Layers — Don't treat patterns as isolated solutions. The most robust agents combine multiple patterns: "Episodic Memory Retrieval & Injection" for context, "Plan-Then-Execute" for structure, "Circuit Breaker" for reliability, and "Self-Critique Evaluator Loop" for quality. The website's Graph Visualization helps identify natural pattern clusters.

Match Pattern Complexity to Task Risk — Not every agent needs every pattern. A simple classification agent might only need "Structured Output Specification." A autonomous coding agent deploying to production needs the full stack. Use the Decision Explorer to avoid over-engineering and under-engineering alike.

Implement Pattern Telemetry — When you adopt a pattern, instrument it. The "LLM Observability" pattern isn't just for model calls—apply it to pattern execution itself. Track which patterns trigger, their success rates, and their computational overhead. This data feeds the "Incident-to-Eval Synthesis" pattern, creating virtuous improvement cycles.

Contribute Your Variations — The repository's contribution guidelines encourage patterns that are "repeatable" and "traceable." If you've adapted a pattern for a novel domain or discovered an edge case, document it. The llms.txt infrastructure means your contribution becomes discoverable by AI assistants worldwide, amplifying impact beyond human readers.

Version Your Pattern Implementations — As the catalogue evolves, pattern definitions refine. Pin your dependencies on specific pattern versions, and review updates during planned maintenance windows. The "Versioned Constitution Governance" pattern applies to your own agent's rule sets, not just the patterns you adopt.

Comparison with Alternatives

The fundamental differentiator is pattern-centricity versus tool-centricity. Frameworks teach you their way; awesome-agentic-patterns teaches you the underlying design space, letting you adapt patterns to any framework or custom implementation. It's the difference between learning a specific library and learning software engineering principles.

FAQ: What Developers Ask About Agentic Patterns

Q: Is awesome-agentic-patterns a framework I install, or just documentation? A: It's primarily a knowledge catalogue—curated documentation of proven patterns. You implement the patterns in your preferred framework (LangChain, LlamaIndex, custom code, etc.). However, the repository includes tooling for generating documentation and a reference web implementation you can study or extend.

Q: How do I know which patterns to adopt first? A: Start with the Decision Explorer on the website. For most production agents, prioritize: Context & Memory patterns (agents fail without coherent state), Reliability & Eval patterns (you can't improve what you don't measure), and one Feedback Loop pattern (continuous improvement is essential). Add Orchestration patterns as complexity demands.

Q: Can I use these patterns with any LLM provider? A: Absolutely. The patterns are model-agnostic architectural guidance. Some patterns like "Budget-Aware Model Routing with Hard Cost Caps" explicitly assume multiple model access, but most apply regardless of whether you use OpenAI, Anthropic, open-source models, or a mix.

Q: What's the difference between a "pattern" and a "prompt technique"? A: Prompt techniques (chain-of-thought, few-shot, etc.) operate at the single-interaction level. Agentic patterns span the full system lifecycle—memory management across sessions, multi-agent coordination, feedback integration over time, security boundaries, and operational tooling. They're architectural, not tactical.

Q: How current is the pattern catalogue? A: The auto-generation pipeline means patterns are added as soon as PRs merge. The repository shows active contribution velocity, and the llms.txt file updates with each build. Check the star history graph on the repository for growth trajectory.

Q: Is there commercial support or consulting around these patterns? A: The project is Apache-2.0 open source with community maintenance. For production implementations, the traceability requirement means most patterns link to original sources—often companies sharing their experiences. Follow those breadcrumbs for deeper engagement with specific pattern authors.

Q: How do I contribute a pattern from my production system? A: Fork, branch, add your pattern file under patterns/, run bun run build:data, and open a PR. Ensure your pattern meets the three criteria: repeatable, agent-centric, traceable. The maintainers are strict about product promotions—focus on the architectural insight, not your company.

Conclusion: The Pattern Literacy Imperative

The AI agent landscape is experiencing a brutal talent stratification. On one side, developers building toy demos that impress on social media but collapse under production load. On the other, engineers who've internalized pattern literacy—the ability to recognize recurring problems, apply proven solutions, and compose patterns into robust systems. awesome-agentic-patterns is the fastest path from the first camp to the second.

This catalogue doesn't just save you from reinventing wheels—it saves you from wheels that were never round to begin with. The patterns here represent collective intelligence from teams that have shipped, failed, recovered, and documented so you don't have to repeat their painful discoveries. From context management that actually preserves coherence, to feedback loops that turn failures into improvements, to security boundaries that let you sleep through the night—this is the infrastructure of professional agent engineering.

The repository is actively growing, the web platform is genuinely useful, and the llms.txt integration means it's becoming infrastructure for the next generation of AI-assisted development itself. But the patterns only work if you use them.

Stop building broken agents. Stop debugging problems that already have solutions. Stop pretending that a bigger model will fix your architectural gaps.

Visit awesome-agentic-patterns on GitHub, explore the patterns at agentic-patterns.com, and start shipping agents that actually work in production. Your future self—the one not getting paged at 3 AM—will thank you.

Found this valuable? Star the repository, share it with your team, and consider contributing patterns from your own production experience. The agent engineering community grows stronger when we share what actually works.