Your AI agent works beautifully in development. Responses are quick, conversations flow naturally, and everything feels magical. Then you deploy to production with real users, and suddenly everything breaks.

Response times spike to 5+ seconds. Agents lose conversation context mid-workflow. Memory usage explodes. Users report inconsistent behavior. Your costs skyrocket.

I’ve built AI agent systems that handle 100+ concurrent users with sub-2-second response times. Here’s what actually works in production—and what fails spectacularly.

---

The Development vs. Production Gap

In development, you have:

• One user (you)
• Clean test data
• No concurrent requests
• Unlimited time to respond
• Generous error margins

In production, you face:

• Hundreds of simultaneous users
• Messy, unpredictable inputs
• Race conditions everywhere
• Users expect <2s responses
• Every error costs trust (and money)

The patterns that work in development often collapse under production load. Here’s how to build agents that scale.

---

Pattern 1: Goal-Oriented Agents with Explicit Completion

The Problem

Most agents don’t know when they’re done. They keep talking, asking questions, or offering help even after achieving their goal. This creates confused users and wasted tokens.

Consider an agent building a quality plan:

• User: “Create a quality plan for Project Alpha”
• Agent: asks 8 clarifying questions, gathers data, generates plan
• Agent: “I’ve created your plan. Would you like me to explain each section? Should I also create an SOP? How about maintenance schedules?”

The agent succeeded but doesn’t know it. The conversation drifts instead of completing cleanly.

The Solution: Explicit Completion Signals

Design agents with clear goals and completion markers:

SYSTEM_PROMPT = """
You are a Quality Planning Agent.

YOUR GOAL: Create ONE quality plan for the user's project.

WORKFLOW:
1. Gather project requirements
2. Identify quality checkpoints
3. Map inspection criteria
4. Generate the plan using create_quality_plan()
5. Output: [TASK_COMPLETE]

CRITICAL: After successfully creating the plan, you MUST output [TASK_COMPLETE]
This signals that your work is finished.

Do not:
- Offer additional services
- Start new tasks
- Continue the conversation after completion
"""

The orchestrator watches for this signal:

def check_completion(agent_response: str) -> bool:
return '[TASK_COMPLETE]' in agent_response

def extract_clean_response(agent_response: str) -> str:
# Remove marker before showing to user
return agent_response.replace('[TASK_COMPLETE]', '').strip()

Why This Works

✅ Agents know their scope: Each agent has ONE job, not infinite capabilities

✅ Clear boundaries: The agent completes its task and returns control to the orchestrator

✅ Better UX: Users get what they asked for without unnecessary follow-ups

✅ Composability: Completed agents can trigger suggested next actions

Real-World Impact

Before explicit completion:

• Average conversation: 18 turns
• Task completion rate: 73%
• Users confused about status

After explicit completion:

• Average conversation: 8-12 turns
• Task completion rate: 94%
• Clear status for users and system

---

Pattern 2: Context Isolation by Task

The Problem

Agents accumulate context that becomes noise for future tasks. Consider this scenario:

1. User creates a quality plan (agent loads machines, materials, specs)
2. User switches to maintenance scheduling (agent still has quality plan context)
3. Agent confuses quality checkpoints with maintenance tasks
4. Results are mixed and incorrect

The context from Task A pollutes Task B. As conversations grow, this gets worse.

The Solution: Project-Based Context Windows

Isolate context to what’s relevant for the current task:

class ContextManager:
def build_agent_context(self, task_type: str, project_id: str) -> dict:
"""
Load only the context needed for this specific task.
"""
base_context = {
'project_name': self.get_project_name(project_id),
'timestamp': datetime.now()
}

# Task-specific context
if task_type == 'quality_planning':
return {
**base_context,
'machines': self.get_machines(project_id),
'materials': self.get_materials(project_id),
'specs': self.get_specifications(project_id)
}

elif task_type == 'maintenance_scheduling':
return {
**base_context,
'machines': self.get_machines(project_id),
'maintenance_history': self.get_history(project_id),
'upcoming_schedules': self.get_schedules(project_id)
}

elif task_type == 'sop_creation':
return {
**base_context,
'workstations': self.get_workstations(project_id),
'resources': self.get_resources(project_id),
'takt_time': self.get_takt_time(project_id)
}

# Only load what you need
return base_context

Context Boundaries

Within a session: Agent remembers conversation history for current task only.

Between tasks: Fresh context window when switching tasks.

Cross-task references: Explicit handoffs with minimal context transfer.

Why This Works

✅ Reduced noise: Agent sees only relevant information

✅ Faster responses: Smaller context = lower latency

✅ Lower costs: Fewer tokens per request

✅ Better accuracy: No confusion from irrelevant data

---

Pattern 3: LLM-Based Intent Routing

The Problem

Users don’t announce which agent they need. They just describe their problem:

• “I need to plan quality checkpoints” → Quality Planning Agent
• “When was Machine A last serviced?” → Maintenance Agent
• “Create work instructions for Station 3” → SOP Agent

Keyword matching fails because users phrase things differently. ML classifiers require training data and struggle with new variations.

The Solution: LLM as Router

Use an LLM to understand intent and route to the appropriate agent:

class IntentRouter:
def __init__(self, llm_client):
self.llm = llm_client

async def route(self, user_message: str, context: dict) -> str:
"""
Analyze user intent and return appropriate agent key.
"""

routing_prompt = f"""
Analyze this user message and determine which specialized agent should handle it.

AVAILABLE AGENTS:

1. quality_planning - Creates quality plans, inspection checklists, PM plans
Examples: "create quality plan", "plan inspections", "quality checkpoints"

2. maintenance_scheduling - Manages preventive maintenance schedules
Examples: "maintenance schedule", "when to service machines", "PM tracking"

3. sop_creation - Generates standard operating procedures
Examples: "create SOP", "work instructions", "procedure for assembly"

4. issue_tracking - Handles problem reporting and resolution
Examples: "report issue", "quality problem", "defect tracking"

5. general - Unclear intent, chitchat, or requests outside scope

USER MESSAGE: "{user_message}"

PROJECT SELECTED: {context.get('project_id') is not None}

Respond with ONLY the agent key (quality_planning, maintenance_scheduling, etc.)
"""

response = await self.llm.complete(routing_prompt)
agent_key = response.strip().lower()

# Validate response
valid_agents = ['quality_planning', 'maintenance_scheduling',
'sop_creation', 'issue_tracking', 'general']

if agent_key not in valid_agents:
return 'general'  # Safe fallback

return agent_key

Why LLM Routing Works

✅ Zero-shot learning: No training data required

✅ Natural language understanding: Handles variations and synonyms naturally

✅ Easy to extend: Add new agents by updating the prompt

✅ Context-aware: Can consider project state, user history, etc.

✅ Fast enough: 300-500ms routing decision is acceptable

Routing Performance

In production:

• Accuracy: 95%+ correct routing
• Latency: 400-600ms average
• False positives: <3%
• Ambiguous handling: Routes to general agent for clarification

The 5% errors are usually genuinely ambiguous requests that need clarification anyway.

---

Pattern 4: The Orchestrator Pattern

The Problem

Who coordinates multiple specialized agents? If agents call each other directly, you get spaghetti architecture. If they’re independent, you can’t compose workflows.

The Solution: Central Orchestrator

One orchestrator manages all agents and workflow transitions:

class Orchestrator:
def __init__(self, session_manager, router, agent_registry):
self.sessions = session_manager
self.router = router
self.agents = agent_registry

async def handle_message(self, session_id: str, user_message: str, context: dict):
"""
Main entry point. Routes and coordinates agent execution.
"""

# Get session state
session = await self.sessions.get(session_id)

# Check current mode
if session['mode'] == 'orchestrator':
# No active task - route to appropriate agent
agent_key = await self.router.route(user_message, context)

if agent_key == 'general':
return await self.handle_general(user_message)

# Start new task with specialized agent
session['mode'] = 'task_active'
session['active_agent'] = agent_key
await self.sessions.update(session)

# Task is active - continue with current agent
agent = await self.get_agent(session['active_agent'], context)
response = await agent.process(user_message)

# Check if task completed
if self.is_complete(response):
# Return to orchestrator mode
session['mode'] = 'orchestrator'
session['active_agent'] = None
await self.sessions.update(session)

# Suggest next actions
suggestions = self.get_suggestions(session['active_agent'])

return {
'response': self.clean_response(response),
'suggestions': suggestions,
'task_complete': True
}

# Task ongoing
return {
'response': response,
'task_complete': False
}

Orchestrator Responsibilities

1. Intent Routing

• Analyzes user message
• Selects appropriate agent
• Handles ambiguity

2. State Management

• Tracks orchestrator vs. task-active mode
• Manages active agent per session
• Persists conversation history

3. Task Completion

• Detects completion signals
• Returns control to orchestrator
• Suggests next actions

4. Error Handling

• Catches agent failures
• Provides graceful degradation
• Maintains system stability

State Transitions

[Orchestrator Mode]
↓
User Message
↓
Intent Routing
↓
[Task Active Mode] → Agent Processing
↓                    ↑
Task Complete?          |
↓ (No)──────────────┘
↓ (Yes)
Suggested Actions
↓
[Orchestrator Mode]

Why This Works

✅ Single source of truth: Orchestrator owns session state

✅ Clean agent APIs: Agents only handle domain logic, not coordination

✅ Composability: Easy to add new agents to the registry

✅ Testability: Each component can be tested independently

✅ Debuggability: All routing decisions go through one place

---

Pattern 5: Off-Topic Detection with Context Preservation

The Problem

Users naturally drift during conversations:

User: "Create a quality plan for Project X"
Agent: "What product are you manufacturing?"
User: "Automotive parts. By the way, when is lunch?"
Agent: "I don't have information about lunch schedules..."

Should the agent:

• Stay rigid? (Poor UX)
• Answer everything? (Loses focus)
• Redirect immediately? (Feels robotic)

The Solution: Conservative Off-Topic Detection

Detect genuine topic switches while allowing natural conversation flow:

class OffTopicDetector:
async def check(self, user_message: str, active_agent: str,
conversation_history: list) -> tuple[bool, str]:
"""
Returns: (is_off_topic, suggested_new_agent)
"""

agent_goals = {
'quality_planning': 'creating a quality plan or PM plan',
'maintenance_scheduling': 'scheduling preventive maintenance',
'sop_creation': 'creating standard operating procedures',
'issue_tracking': 'reporting and tracking quality issues'
}

current_goal = agent_goals.get(active_agent)

detection_prompt = f"""
Current Task: {current_goal}

Recent Conversation:
{self._format_history(conversation_history[-3:])}

New User Message: "{user_message}"

Question: Is this message clearly switching to a DIFFERENT, UNRELATED task?

Guidelines:
- Clarifying questions about current task = ON TOPIC
- Requesting changes to current work = ON TOPIC
- Small tangents that relate back = ON TOPIC
- Starting entirely new unrelated task = OFF TOPIC

Examples:
ON TOPIC:
- "Can you explain what you mean by checkpoint?"
- "Actually, use Machine B instead of Machine A"
- "Wait, I need to add one more material"

OFF TOPIC:
- "Actually, let's work on maintenance scheduling instead"
- "I need to report a quality issue"
- "Create an SOP for me"

Respond: ON_TOPIC or OFF_TOPIC|suggested_agent_key
"""

response = await self.llm.complete(detection_prompt)

if response.startswith('OFF_TOPIC'):
parts = response.split('|')
suggested_agent = parts[1] if len(parts) > 1 else 'general'
return True, suggested_agent

return False, None

Graceful Topic Switching

When off-topic detected, give users choice:

if is_off_topic and suggested_agent:
return {
'response': (
f"I notice you want to switch to {suggested_agent}. "
f"Would you like to:\n"
f"1. Complete the current task first\n"
f"2. Switch now (we can return to this later)\n"
f"3. Cancel current task"
),
'requires_choice': True
}

Why Conservative Detection Works

✅ Few false positives: Natural conversation continues smoothly

✅ Clear boundaries: Genuine topic switches are caught

✅ User control: Let users decide how to handle switches

✅ Context preservation: Can return to incomplete tasks later

In testing:

• 91% of clarifications correctly allowed
• 97% of topic switches correctly detected
• User satisfaction significantly higher than rigid systems

---

Pattern 6: Tool Call Orchestration and Validation

The Problem

Agents call tools, but tools can fail:

• Rate limits
• Invalid parameters
• Missing data
• Timeout errors
• Unexpected responses

Poor tool orchestration leads to:

• Agent hallucinating tool results
• Incomplete workflows
• User confusion
• Data inconsistencies

The Solution: MCP (Model Context Protocol) Pattern

Create a controlled tool layer between agents and APIs:

class ToolOrchestrator:
def __init__(self, api_client):
self.api = api_client
self.validators = self._setup_validators()

async def execute_tool(self, tool_name: str, parameters: dict) -> dict:
"""
Validate, execute, and handle tool calls with proper error recovery.
"""

# Pre-execution validation
validation_result = self.validators[tool_name](parameters)
if not validation_result.valid:
return {
'success': False,
'error': f"Invalid parameters: {validation_result.error}",
'suggestion': validation_result.fix_suggestion
}

# Execute with retry logic
for attempt in range(3):
try:
result = await self.api.call(tool_name, parameters)

# Post-execution validation
if self._validate_result(tool_name, result):
return {
'success': True,
'data': result
}

except RateLimitError:
if attempt < 2:
await asyncio.sleep(2 ** attempt)
continue
return {
'success': False,
'error': 'Rate limit exceeded. Please try again in a moment.'
}

except TimeoutError:
if attempt < 2:
continue
return {
'success': False,
'error': 'Request timed out. The operation may still complete.'
}

except InvalidDataError as e:
return {
'success': False,
'error': f'Data validation failed: {str(e)}',
'suggestion': 'Please check your input parameters'
}

return {
'success': False,
'error': 'Maximum retry attempts reached'
}

Tool Validation Strategy

Pre-execution checks:

• Required parameters present
• Parameter types correct
• Values within expected ranges
• Dependencies available

Post-execution checks:

• Response structure matches expected format
• Data integrity validated
• Side effects confirmed
• Error conditions handled

Agent Tool Error Handling

Agents receive tool results and adapt:

# In agent system prompt
"""
When using tools:

1. Check tool result success status
2. If failure, read the error message
3. Follow any suggestions provided
4. Retry with corrected parameters if applicable
5. If unable to proceed, explain to user what went wrong

Example:
Tool result: {'success': False, 'error': 'Machine X not found in project'}
Your response: "I couldn't find Machine X in this project. Could you verify
the machine name or select from: [list available machines]"
"""

Why This Pattern Works

✅ Controlled access: Tools can’t be misused by agents

✅ Graceful degradation: Errors don’t crash the agent

✅ Clear feedback: Agents understand what went wrong

✅ Retry logic: Transient failures resolved automatically

✅ Security: Input validation prevents injection attacks

---

Pattern 7: Conversation History Management

The Problem

LLMs have token limits. Long conversations exceed context windows:

• 20-turn conversation = 8,000+ tokens
• System prompt = 1,500 tokens
• Tool definitions = 2,000 tokens
• Project context = 1,000 tokens
• Total: 12,500 tokens (near limit for many models)

What happens at message 21?

The Solution: Smart History Windowing

Keep recent context + summarize old messages:

class ConversationManager:
def __init__(self, max_full_messages=8):
self.max_full_messages = max_full_messages

async def prepare_context(self, session_id: str) -> list:
"""
Prepare conversation history for agent, managing token budget.
"""

full_history = await self.get_history(session_id)

if len(full_history) <= self.max_full_messages:
return full_history

# Keep recent messages
recent = full_history[-self.max_full_messages:]

# Summarize older messages
older = full_history[:-self.max_full_messages]
summary = await self._create_summary(older)

return [
{
'role': 'system',
'content': f'Previous conversation summary: {summary}'
},
*recent
]

async def _create_summary(self, messages: list) -> str:
"""
Create concise summary of older messages.
"""

conversation_text = '\n'.join([
f"{msg['role']}: {msg['content']}"
for msg in messages
])

summary_prompt = f"""
Summarize this conversation in 2-3 sentences, focusing on:
- Key decisions made
- Data collected
- Current progress toward goal

Conversation:
{conversation_text}

Summary:
"""

summary = await self.llm.complete(summary_prompt)
return summary.strip()

When to Summarize

Option 1: Fixed window

• Keep last N messages (e.g., 8-10)
• Summarize everything before that
• Simple and predictable

Option 2: Token-aware

• Count tokens in current context
• Summarize when approaching 80% of limit
• More efficient but complex

Option 3: Task-based

• Full history during active task
• Summarize on task completion
• Keeps task context intact

What to Keep vs. Summarize

Always keep:

• System prompt
• Tool definitions
• Last 3-5 messages (current context)
• Active task data

Can summarize:

• Old clarifying questions
• Resolved issues
• Completed sub-tasks
• General chitchat

Never summarize:

• Critical data user provided
• Tool call results needed for current task
• Error messages that might recur

---

Real-World Architecture: Putting It Together

Here’s how these patterns combine in production:

User Message
↓
┌────────────────────┐
│   Orchestrator     │
│  (Entry Point)     │
└─────────┬──────────┘
│
Session State?
┌─────┴─────┐
│           │
Orchestrator  Task Active
Mode         Mode
│           │
↓           ↓
┌─────────┐  ┌──────────┐
│ Intent  │  │ Current  │
│ Router  │  │ Agent    │
│ (LLM)   │  │          │
└────┬────┘  └────┬─────┘
│            │
↓            ↓
┌────────────────────┐
│  Agent Registry    │
│  - Quality Agent   │
│  - Maintenance     │
│  - SOP Agent       │
│  - Issue Tracker   │
└─────────┬──────────┘
│
↓
┌───────────────────┐
│  Context Manager  │
│  (Task-specific)  │
└─────────┬─────────┘
│
↓
┌───────────────────┐
│  Tool Orchestrator│
│  (MCP Pattern)    │
└─────────┬─────────┘
│
↓
┌───────────────────┐
│  Completion Check │
│  [TASK_COMPLETE]  │
└─────────┬─────────┘
│
Complete?
┌─────┴──────┐
Yes          No
│            │
↓            ↓
Suggestions   Continue
Return to     with Agent
Orchestrator

Flow Example: Quality Planning

1. User: “Create a quality plan”
2. Orchestrator: Routes to Intent Router
3. Router: Returns ‘quality_planning’ agent
4. Orchestrator: Activates Quality Planning Agent
5. Context Manager: Loads machines, materials, specs
6. Agent: “What product are you manufacturing?”
7. User: “Automotive parts”
8. Agent: Processes, calls tools, generates plan
9. Agent: “Plan created. [TASK_COMPLETE]”
10. Orchestrator: Detects completion, returns to orchestrator mode
11. System: Suggests: “Create SOP?” “Schedule maintenance?”

---

Key Takeaways

Production-grade agents require structured patterns:

✅ 1. Goal-Oriented Design

• Each agent has ONE clear objective
• Explicit completion signals
• No scope creep

✅ 2. Context Isolation

• Task-specific context loading
• No cross-contamination
• Fresh starts for new tasks

✅ 3. Intelligent Routing

• LLM-based intent understanding
• 95%+ accuracy in production
• Handles natural language variations

✅ 4. Central Orchestration

• One coordinator for all agents
• Clear state management
• Composable workflow design

✅ 5. Conservative Topic Detection

• Allow natural conversation flow
• Catch genuine topic switches
• User control over transitions

✅ 6. Validated Tool Execution

• MCP pattern for controlled access
• Pre and post-execution validation
• Graceful error recovery

✅ 7. Smart History Management

• Token-aware windowing
• Summarization of old context
• Preserve critical information

---

Common Anti-Patterns to Avoid

❌ Autonomous agents with no structure → Agents wander, lose focus, never complete

❌ Shared context across all tasks → Confusion, mixed data, poor accuracy

❌ Keyword-based routing → Brittle, can’t handle variations, high error rate

❌ Direct agent-to-agent communication → Spaghetti architecture, hard to debug

❌ Ignoring off-topic detection → Agents follow users down rabbit holes

❌ Trusting tool calls blindly → Cascading failures, poor error messages

❌ Unlimited conversation history → Token limit errors, high costs, crashes

---

The Bottom Line

Building production-grade AI agents isn’t about autonomy—it’s about architecture.

What works:

• Specialized agents with clear goals
• Explicit completion signals
• Task-isolated context
• LLM-based routing
• Central orchestration
• Validated tool execution
• Managed conversation history

What fails:

• Generic autonomous agents
• Implicit task completion
• Shared global context
• Rule-based routing
• Direct agent coupling
• Unvalidated tool calls
• Unlimited history

The agents that work in production have structure. They know their goals, understand their boundaries, and complete tasks reliably.

That’s what production-grade means.

About the Author

I build production-grade multi-agent systems for manufacturing, sales, and productivity automation. My agents follow structured workflows with 94% task completion rates, achieving 75% reduction in manual work time.

Specialized in orchestration patterns, context management, and LLM-based routing using CrewAI, Agno, and custom architectures.

Open to consulting and technical partnerships. Let’s discuss your agent architecture challenges!

📧 Contact: gupta.akshay1996@gmail.com

Found this helpful? Share it with other AI builders! 🚀

What production challenges are you facing with AI agents? Drop a comment below!