How to Host Multiple AI Agents on a Single Domain with Analytics

Introduction

You've built several AI agents. Each one does something specific—one handles customer support, another processes documents, a third analyzes data. Now you need to deploy them all under one domain and track how they're performing.

This isn't straightforward. AI agents need different resources, they interact with various systems, and they require continuous monitoring to ensure they're working correctly. Put them all on one domain without proper infrastructure and you'll face deployment conflicts, performance bottlenecks, and zero visibility into what's actually happening.

This guide walks through the technical decisions you need to make: how to structure your deployment, which infrastructure patterns work best, what observability tools to implement, and how to set up analytics that actually help you understand agent behavior. We'll cover production-ready approaches that work for teams running 3 agents or 30.

By the end, you'll know how to deploy multiple AI agents under a single domain with proper isolation, monitoring, and analytics—without needing a full DevOps team to maintain it.

Understanding Multi-Agent System Requirements

Multiple AI agents under one domain means more than just putting different services behind the same URL. Each agent needs its own execution environment, but they also need to share infrastructure efficiently.

What Makes Multi-Agent Deployments Different

AI agents differ from traditional microservices in three important ways. First, they're non-deterministic. The same input can produce different outputs depending on model behavior, context, and tool availability. This makes debugging harder and requires different monitoring approaches.

Second, AI agents execute code dynamically. They call APIs, query databases, and run functions based on reasoning steps you can't predict in advance. This creates security and isolation requirements that standard container orchestration doesn't fully address.

Third, agents maintain state across interactions. They remember context, store conversation history, and build up knowledge over time. This stateful nature means you can't just scale them horizontally like stateless services.

These characteristics mean your infrastructure needs to handle unpredictable resource usage, provide strong isolation between agents, support long-running processes, and maintain persistent state reliably.

Core Infrastructure Components

A production multi-agent system needs five infrastructure layers. The execution layer runs agent code in isolated environments. The orchestration layer coordinates agent interactions and manages workflows. The state layer handles persistent data like conversation history and agent memory. The communication layer enables agents to interact with each other and external systems. The observability layer tracks what agents are doing and how they're performing.

Most teams underestimate the state and observability layers. Agents that can't remember context across sessions feel broken to users. Agents without proper monitoring become black boxes that fail in ways you can't diagnose.

Domain Architecture Patterns

You have three main options for hosting multiple agents under one domain. The gateway pattern puts all agents behind a single API gateway that routes requests based on path or header. The subdomain pattern assigns each agent its own subdomain. The path-based pattern uses URL paths to distinguish between agents.

The gateway pattern offers the most flexibility. You can route based on complex logic, implement rate limiting per agent, and change backend infrastructure without affecting URLs. It does add a single point of failure and slight latency overhead.

The subdomain pattern provides natural isolation and makes it easier to apply different security policies per agent. It requires managing DNS and SSL certificates for each subdomain.

The path-based pattern is simplest to implement but limits your routing options and makes it harder to apply agent-specific configurations.

Most production systems use the gateway pattern with an intelligent router that can direct requests to the right agent based on intent, context, or load conditions.

Deployment Infrastructure Options

Where you run your agents matters. The choice affects cost, operational complexity, and what capabilities you can offer.

Kubernetes for Multi-Agent Systems

Kubernetes has become standard for deploying AI agents at scale. It provides declarative configuration, automatic scaling, self-healing infrastructure, and consistent deployment across environments.

For AI agents specifically, Kubernetes offers several advantages. You can define each agent as a custom resource with proper RBAC, resource limits, and tool integrations. The pod model naturally supports agent isolation while allowing shared resources like databases and vector stores.

Kubernetes enables independent scaling of agents based on load. Your customer support agent might need 10 replicas during business hours while your analytics agent runs as a single instance. Horizontal pod autoscaling adjusts resources automatically based on metrics you define.

The main complexity with Kubernetes is configuration overhead. You need to understand pods, services, deployments, ingress controllers, and persistent volumes. For teams without Kubernetes experience, the learning curve is steep.

GPU resource management adds another layer of complexity. If your agents use local models, you need to configure GPU sharing or Multi-Instance GPU to avoid dedicating entire GPUs to single agents. Tools like NVIDIA's device plugin help but require careful tuning.

Serverless Container Deployment

Serverless containers offer automatic scaling without infrastructure management. Services like AWS Fargate, Google Cloud Run, and Azure Container Instances let you deploy containerized agents that scale to zero when idle and automatically provision resources during traffic spikes.

This works well for agents with unpredictable or bursty traffic patterns. A document processing agent might sit idle for hours then suddenly receive 100 requests. Serverless containers handle this without manual intervention or wasted resources.

The limitations matter for certain agent types. Cold start latency ranges from 1-5 seconds depending on container size and region. For real-time chat agents, this delay breaks the user experience. Execution timeouts (typically 15 minutes) don't work for long-running agent workflows that might take hours to complete.

Memory and CPU limits can constrain agent capabilities. If your agent loads large models or processes complex reasoning chains, you might hit platform limits. Serverless containers also lack support for persistent processes—agents restart on every invocation, losing in-memory state.

Hybrid Infrastructure Approaches

Most production deployments combine approaches. Critical, latency-sensitive agents run on dedicated infrastructure with guaranteed resources. Batch processing agents use spot instances or serverless to minimize cost. Background agents that update caches or process queues run as Kubernetes CronJobs.

The key is matching infrastructure to agent characteristics. Agents with steady load and strict latency requirements need dedicated resources. Agents with variable load benefit from autoscaling. Agents that can tolerate interruption run cheaply on spot instances.

This hybrid approach requires more sophisticated routing. Your API gateway needs health checks for each infrastructure type, fallback logic when primary infrastructure is unavailable, and request queuing for agents that scale slowly.

Agent Isolation and Resource Management

Running multiple agents under one domain means preventing them from interfering with each other while sharing infrastructure efficiently.

Sandboxing Agent Execution

Agent isolation prevents one agent from accessing another's data or affecting its performance. The level of isolation you need depends on your security requirements and agent capabilities.

Process-level isolation is minimal but fast. Each agent runs as a separate process with its own memory space. This prevents accidental data sharing but doesn't protect against malicious code or resource exhaustion.

Container-level isolation provides stronger boundaries. Each agent runs in its own container with defined resource limits and network policies. This prevents resource contention and adds security through namespace isolation.

VM-level isolation offers the strongest security. Technologies like gVisor or Kata Containers provide near-VM isolation with container-like overhead. This matters for agents that execute untrusted code or have strict compliance requirements.

WebAssembly sandboxing is gaining traction for certain agent types. Wasm provides strong memory isolation, fine-grained capability control, and sub-millisecond startup times. The tradeoff is limited language support and ecosystem maturity.

Resource Allocation Strategies

AI agents have unpredictable resource needs. A simple Q&A agent might use 100MB of memory while a reasoning agent processing complex queries could spike to 8GB.

Static resource allocation wastes capacity. If you provision for peak usage across all agents, you'll pay for idle resources most of the time. Dynamic allocation lets agents scale up when needed but requires monitoring to prevent resource exhaustion.

The approach that works: set guaranteed minimums and allow bursting to limits. Each agent gets a minimum allocation that ensures basic functionality. When load increases, agents can burst to higher limits if resources are available. This balances cost with reliability.

CPU and memory limits prevent runaway agents from affecting others. Set reasonable limits based on agent type—chat agents might need 1-2 CPU cores while batch processing agents can use more. Memory limits should account for model size, context window, and tool execution overhead.

GPU sharing requires special handling. Unless your agents use cloud-hosted models exclusively, you'll need to share GPU resources. Time-slicing lets multiple agents share GPU cycles. Multi-Instance GPU partitions physical GPUs into isolated instances. The right choice depends on your workload—time-slicing works for light inference while MIG suits workloads needing guaranteed performance.

Network Policies and Communication Patterns

Agents need to communicate with external systems, shared resources, and sometimes each other. Network policies define what each agent can access.

Start with deny-all policies then explicitly allow required connections. A customer support agent needs access to your CRM API and knowledge base but shouldn't reach your financial systems. A data analysis agent needs database access but doesn't need internet connectivity.

Service meshes like Istio or Linkerd add observability and security to agent communication. They provide automatic mTLS between services, fine-grained access control, traffic shaping, and detailed telemetry. The operational overhead is significant but worthwhile for large deployments.

For agent-to-agent communication, protocols like MCP and A2A provide standardized interfaces. MCP focuses on tool access—giving agents consistent ways to call external capabilities. A2A enables agents to discover and coordinate with each other.

Setting Up Centralized Analytics

Analytics for AI agents goes beyond traditional application metrics. You need to track not just performance but behavior, decision-making, and business impact.

Implementing Agent Observability

Observability for AI agents means understanding what they're doing and why. Traditional monitoring tracks errors and latency. Agent observability tracks reasoning steps, tool usage, model selection, and decision quality.

Distributed tracing is essential. Each agent workflow becomes a trace with spans representing individual operations—model calls, tool executions, retrieval steps, reasoning phases. OpenTelemetry provides vendor-neutral instrumentation that works across agent frameworks.

A typical trace might show: user query received, query classified by router agent, relevant context retrieved from vector store, reasoning model invoked with context, tool execution triggered, response validated, final output returned. Each step includes timing, inputs, outputs, and metadata like token count or model used.

The challenge is capturing enough detail without drowning in data. Start with critical paths—user-facing interactions, tool executions, expensive operations. Add instrumentation incrementally as you identify gaps in understanding.

Metrics That Actually Matter

Standard application metrics apply to agents—request rate, error rate, latency percentiles. But AI agents need additional metrics that capture their unique characteristics.

Token usage matters for cost and context management. Track tokens per request, cumulative tokens per session, and breakdown by model. This helps identify inefficient agents that use expensive models unnecessarily or waste tokens on poor prompts.

Tool invocation patterns reveal agent behavior. Which tools get called most often? Which tools fail frequently? Are agents calling tools in expected sequences? Unexpected patterns often indicate prompt problems or missing capabilities.

Context window utilization shows how much of available context agents actually use. Agents that consistently hit context limits need architecture changes—chunking, summarization, or different retrieval strategies.

Quality metrics measure agent output correctness. For deterministic tasks, track accuracy against known answers. For subjective tasks, implement LLM-based evaluation where another model judges response quality. Track user feedback signals like thumbs up/down, message refinements, or conversation abandonment.

Business metrics tie agent activity to outcomes. For customer support agents, track resolution rate, escalation rate, and user satisfaction. For sales agents, track qualified leads and conversion rate. These metrics justify agent investment and guide improvement efforts.

Building Analytics Dashboards

Raw metrics mean nothing without visualization and context. Analytics dashboards make agent behavior visible to different stakeholders.

Operations teams need real-time monitoring. Show current request volume, error rates, and resource usage. Alert on anomalies like sudden traffic spikes, error rate increases, or resource exhaustion. Include agent-specific views so operators can quickly identify which agents have problems.

Development teams need debugging tools. Session replay lets them see exact agent behavior for specific requests. Trace visualization shows decision trees and timing breakdowns. Prompt versioning history helps track changes that affected behavior.

Product teams need usage analytics. Show agent adoption over time, user engagement patterns, and feature usage. Compare agent variants to see which approaches work better. Track business metrics alongside technical metrics to understand impact.

The dashboards that get used have three characteristics: they load fast, they answer specific questions, and they enable action. Avoid creating comprehensive dashboards that show everything—create focused views for specific use cases.

Log Aggregation and Search

Logs capture details that metrics can't. Agent reasoning steps, tool outputs, error messages, and user interactions all belong in logs.

Structured logging makes logs searchable. Use JSON format with consistent field names. Include correlation IDs that link related log entries across services. Add context like user ID, session ID, and agent ID to every log entry.

Centralized log aggregation is mandatory for multi-agent systems. Tools like Elasticsearch, Loki, or cloud-native logging services collect logs from all agents. This enables searching across agents, correlating events, and retaining logs for compliance.

The practical challenge is volume. AI agents generate extensive logs. Implement sampling for verbose operations, separate hot and cold storage for cost optimization, and define retention policies that balance compliance needs with storage costs.

Performance Monitoring and Optimization

Monitoring reveals problems. Optimization fixes them. AI agents have specific performance bottlenecks that require targeted solutions.

Identifying Performance Bottlenecks

Start with end-to-end latency analysis. Break down where time goes—model inference, tool execution, data retrieval, network calls. The Pareto principle applies: 80% of latency typically comes from 20% of operations.

Model inference often dominates. If agents use cloud-hosted models, network latency and queueing add up. If agents use local models, GPU utilization matters. Check if you're maxing out GPU memory or hitting compute limits.

Context retrieval from vector databases can be slow. Monitor query latency, result quality, and cache hit rates. Poor retrieval configuration—wrong similarity metrics, inefficient indexes, or excessive result counts—causes problems.

Tool execution varies widely. API calls to external services might timeout or rate limit. Database queries might scan large tables. File operations might hit disk I/O limits. Profile each tool to understand its performance characteristics.

Caching Strategies

Caching reduces redundant work. AI agents repeat certain operations—embedding queries, common tool calls, frequently accessed documents.

Semantic caching matches similar queries even with different wording. Embed incoming queries, compare to cached embeddings, return cached results if similarity exceeds threshold. This works well for Q&A agents where users ask the same questions in different ways.

Result caching stores expensive operation outputs. Cache tool execution results keyed by input parameters. Cache model responses for deterministic prompts. Set appropriate TTLs based on how frequently underlying data changes.

Context caching stores retrieved information per session. Don't fetch the same documents repeatedly in a conversation. Keep a session-specific cache of retrieved context, tool results, and intermediate reasoning steps.

The tradeoff is stale data. Set cache TTLs based on data freshness requirements. Use cache invalidation for critical data that needs immediate consistency. Monitor cache hit rates to ensure caching provides value.

Model Selection and Routing

Using expensive models for every request wastes money. Intelligent routing directs requests to appropriate models based on complexity.

Semantic routing classifies request intent then routes to suitable models. Simple questions go to fast, cheap models. Complex reasoning tasks use capable models. Implement a classifier that analyzes requests and assigns complexity scores.

Fallback chains provide reliability. If the primary model fails or returns poor results, try a secondary model. This prevents single points of failure and improves success rates. Monitor fallback frequency to identify systematic issues.

Load-based routing distributes requests across model instances. This prevents hotspots and reduces queueing. Implement health checks so traffic shifts away from degraded instances automatically.

Cost-based routing optimizes spending. Track cost per request for different models. Route to cheaper models when quality difference is acceptable. Reserve expensive models for cases where they provide clear value.

Security and Access Control

Multiple agents under one domain means shared security responsibilities. Compromise one agent and attackers might reach others.

Authentication and Authorization

Each agent needs its own identity. Don't use shared credentials across agents. Implement agent-specific service accounts with minimum required permissions.

OAuth2 flows work well for agent authentication. Agents obtain tokens that specify their identity and allowed actions. The token accompanies every request, enabling per-agent access control and audit logging.

For agent-to-agent communication, use mutual TLS or service mesh authentication. This ensures both parties are who they claim and prevents man-in-the-middle attacks.

User authentication happens at the gateway. Validate user credentials before routing to agents. Pass authenticated user context to agents so they can enforce user-level access controls.

Authorization controls what authenticated agents can do. Implement attribute-based access control that checks agent identity, user context, and resource attributes. This prevents agents from accessing data they shouldn't see.

Data Isolation and Privacy

Agents handling sensitive data need isolation. Use separate databases or schemas per tenant. Encrypt data at rest and in transit. Implement data access logging for compliance.

Context and conversation history contain user data. Store them with encryption. Implement retention policies that delete old data. Provide data export and deletion capabilities for privacy compliance.

Tool execution might access sensitive systems. Validate tool inputs to prevent injection attacks. Sanitize outputs before logging. Implement rate limiting to prevent abuse.

Monitoring Security Events

Track authentication failures, authorization violations, and unusual access patterns. These signal potential security issues.

Implement anomaly detection for agent behavior. Sudden changes in request patterns, unusual tool combinations, or access to unexpected resources might indicate compromise.

Regular security audits review agent permissions, access logs, and data handling. Automated tools can flag overprivileged agents or risky configurations.

How MindStudio Simplifies Multi-Agent Deployment

Building and maintaining this infrastructure is complex. MindStudio provides a platform that handles the heavy lifting so you can focus on agent capabilities instead of infrastructure management.

Unified Deployment and Hosting

MindStudio deploys all your agents under a single domain with automatic routing. You don't configure API gateways, set up DNS, or manage SSL certificates. The platform handles infrastructure while you build agents.

Each agent gets its own endpoint under your domain. The system routes requests intelligently based on URL paths or custom routing rules. You can reorganize agents without changing URLs or breaking existing integrations.

Serverless architecture means agents scale automatically. Light traffic uses minimal resources. Traffic spikes get handled without configuration changes. You pay only for actual usage.

Built-In Analytics and Monitoring

MindStudio includes comprehensive analytics without additional setup. Real-time dashboards show agent performance, usage patterns, and cost breakdown. You can drill down to individual sessions to debug problems.

Trace visualization displays complete agent workflows. See exactly what tools got called, which models were used, and where time was spent. This makes optimization straightforward—fix the slowest parts first.

Cost tracking shows spending per agent, per user, and over time. Identify expensive operations and optimize them. Set budgets and get alerts when spending exceeds thresholds.

Security and Access Management

Built-in authentication supports standard identity providers. Users authenticate once and access all agents they're permitted to use. Agent-level permissions control which users can access which capabilities.

SOC 2 certification and GDPR compliance are handled at the platform level. Data encryption, access logging, and retention policies work out of the box. This reduces compliance burden significantly.

Flexible Model Access

MindStudio provides access to over 200 AI models without managing API keys or accounts. Switch between models without code changes. The platform handles routing to appropriate models based on your configuration.

This multi-model approach enables optimization strategies that would require significant infrastructure investment otherwise. Use cheap models for simple tasks and expensive models for complex ones—the platform handles it.

Best Practices for Production Deployments

Getting multiple agents into production requires attention to operational details that aren't obvious until you're already live.

Start Small and Scale Gradually

Don't launch all agents simultaneously. Deploy one agent, monitor its behavior, understand its resource requirements, then add the next. This incremental approach reveals issues before they affect multiple systems.

Begin with lower-risk agents. A document summarization agent causes less damage if it fails than a customer-facing chat agent. Learn on low-stakes agents before deploying critical ones.

Set conservative resource limits initially. It's easier to increase limits than recover from resource exhaustion that affects all agents. Monitor actual usage and adjust based on data.

Implement Comprehensive Error Handling

Agents fail in ways traditional software doesn't. Model APIs timeout, tool executions return unexpected data, reasoning loops hit limits. Every failure mode needs handling.

Implement retry logic with exponential backoff for transient failures. Some operations succeed on second attempt, especially API calls that hit rate limits.

Circuit breakers prevent cascading failures. If a tool consistently fails, stop calling it temporarily. This prevents wasting resources on operations that won't succeed.

Graceful degradation maintains functionality when components fail. If context retrieval fails, agents can still respond based on conversation history. If a preferred model is unavailable, use a fallback model.

Maintain Agent Versioning

Agent behavior changes as you modify prompts, tools, or models. Version control for agents enables rollback when changes cause problems.

Tag agent versions with meaningful identifiers. Track which version is deployed in production. Log agent version with every request for traceability.

Implement canary deployments for changes. Route a small percentage of traffic to new versions, monitor performance, gradually increase traffic if metrics look good. This catches problems before they affect all users.

Document Agent Capabilities

Teams need to know what each agent does, what inputs it expects, and what outputs it produces. This documentation becomes more critical as agent count increases.

Describe agent purpose, supported use cases, expected inputs, and response formats. Include examples of successful interactions and edge cases the agent handles.

Document tool dependencies so teams understand what external systems agents rely on. This helps with debugging when tools fail.

Keep documentation current. Outdated documentation is worse than no documentation because it misleads teams trying to understand system behavior.

Common Pitfalls and How to Avoid Them

Teams deploying multi-agent systems make predictable mistakes. Learning from others saves time and prevents outages.

Underestimating Resource Requirements

AI agents use more resources than most applications. Models consume significant memory, context processing uses CPU cycles, vector operations need GPU access. Underprovisioning leads to poor performance and user complaints.

Load test agents before production deployment. Simulate realistic traffic patterns including spikes. Measure actual resource usage under load. Add headroom for growth and unexpected usage patterns.

Neglecting Context Management

Poor context management breaks agent functionality. Agents lose conversation thread, repeat questions, or provide inconsistent information.

Implement proper session management with unique session IDs. Store conversation history persistently. Include relevant context with every agent invocation. Set reasonable context window limits to prevent memory issues.

Insufficient Monitoring

Operating agents without monitoring means finding out about problems from users instead of proactively fixing them.

Implement monitoring before launch, not after. Set up alerts for critical metrics—error rates, latency spikes, resource exhaustion. Create dashboards that show system health at a glance.

Ignoring Cost Optimization

AI agent costs scale quickly. Token usage, model API calls, and infrastructure expenses add up. Teams often realize costs are unsustainable after deployment.

Track costs from day one. Set budgets per agent and alert when approaching limits. Identify expensive operations and optimize them. Use cheaper models where quality difference is negligible.

Conclusion

Hosting multiple AI agents under a single domain with proper analytics requires careful infrastructure planning, robust monitoring, and continuous optimization. The core requirements are consistent: isolated execution environments, intelligent routing, comprehensive observability, and proper security controls.

The key decisions to make:

• Choose infrastructure that matches your agent characteristics and operational capabilities
• Implement proper isolation to prevent agents from interfering with each other
• Set up distributed tracing and logging from the start
• Track metrics that matter for both technical performance and business impact
• Build security and access control into architecture rather than adding it later

Teams that succeed start small, measure continuously, and iterate based on actual usage patterns. They invest in observability early and treat it as critical infrastructure, not an afterthought.

For teams that want to avoid the infrastructure complexity, platforms like MindStudio handle deployment, routing, monitoring, and analytics so you can focus on building capable agents instead of managing servers. The choice between building your own infrastructure and using a platform depends on your team's capabilities, timeline, and specific requirements.

The multi-agent future is here. The teams that figure out deployment and operations now will have significant advantages as AI agents become standard in more applications.

Frequently Asked Questions

What's the minimum infrastructure needed to host multiple AI agents?

At minimum, you need isolated execution environments for each agent, shared storage for conversation state, an API gateway for routing, and basic monitoring. This can be as simple as separate containers behind an nginx reverse proxy with a shared database. More sophisticated deployments add container orchestration, distributed tracing, and specialized agent infrastructure.

How do I prevent one agent from affecting others when they share infrastructure?

Use container-level isolation at minimum. Each agent runs in its own container with defined CPU and memory limits. Implement network policies that restrict what each agent can access. For stronger isolation, use technologies like gVisor or run agents as separate Kubernetes pods with resource quotas. Monitor resource usage to detect agents consuming excessive resources before they impact others.

What metrics should I track for each agent?

Track request rate, error rate, latency percentiles, token usage, tool invocation patterns, and context window utilization. Add agent-specific metrics based on function—accuracy for Q&A agents, resolution rate for support agents, conversion rate for sales agents. Include cost metrics to understand spending per agent. Use distributed tracing to understand behavior at the workflow level.

How do I handle authentication when multiple agents serve different user groups?

Implement authentication at the gateway level using OAuth2 or similar standards. Pass authenticated user context to agents so they can enforce user-level access controls. Use role-based or attribute-based access control to determine which users can access which agents. Each agent verifies user permissions before processing requests.

Can I run agents on serverless infrastructure if they need persistent state?

Yes, but store state externally in databases or storage services. Serverless functions are stateless by design, so you can't keep state in memory. Use databases for conversation history, storage services for files, and caching layers for frequently accessed data. Design agents to load required state at function start and save state before function termination.

What's the best way to route requests to the right agent?

Use semantic routing that classifies request intent then directs to appropriate agents. Implement a classifier model that analyzes incoming requests and assigns them to agents based on capabilities. Include fallback logic for ambiguous requests. Monitor routing accuracy and retrain classifiers when you see patterns of mis-routing.

How do I debug agents when they're not working correctly?

Implement distributed tracing that captures complete agent workflows. Use trace visualization to see exact execution paths, tool calls, and model responses. Store detailed logs with correlation IDs linking related entries. Implement session replay that shows exact inputs, reasoning steps, and outputs for specific requests. These tools combined let you diagnose most issues.

Should I use a single database for all agents or separate databases?

Use a shared database with proper isolation. Create separate schemas or tables per agent with strict access controls. This provides data isolation while simplifying operational management. For compliance requirements or multi-tenant scenarios, separate databases per agent or tenant might be necessary. Consider operational complexity versus isolation requirements.

How do I handle agents that need GPU access?

Use GPU time-slicing or Multi-Instance GPU to share GPUs across agents. Time-slicing works for light inference workloads. MIG provides stronger isolation and guaranteed performance for agents needing dedicated GPU resources. Monitor GPU utilization to ensure you're not over-provisioning or under-utilizing capacity. Consider cloud-hosted models if GPU management complexity outweighs benefits.

What's the recommended approach for agent-to-agent communication?

Use standardized protocols like MCP for tool access and A2A for agent coordination. Implement message queues for asynchronous communication. Use service mesh for secure synchronous calls. Define clear contracts for agent interactions including message formats, expected behaviors, and error handling. Monitor inter-agent communication patterns to identify inefficiencies or failure modes.