Services
Agentic Optimisation
Zenoware optimizes AI agent performance, reliability, and cost-efficiency. From single-agent workflows to complex multi-agent systems, we identify bottlenecks and implement proven optimizations that deliver measurable improvements.
Why optimise
Agent systems need continuous improvement
AI agents that work in development often struggle in production. Latency issues, unpredictable costs, error handling gaps, and quality inconsistencies emerge at scale. Without systematic optimization, agent systems become expensive and unreliable.
Zenoware applies production-proven techniques to improve agent performance, reduce operational costs, and increase reliability. We measure, analyze, optimize, and validate—delivering concrete improvements backed by metrics.
Optimisation areas
Where we improve agent systems
Comprehensive optimizations across performance, cost, reliability, and system architecture—addressing the full spectrum of agent operational challenges.
Agent Performance Tuning
Improve response quality, reduce latency, and optimize token usage. Prompt engineering, model selection, caching strategies, and request batching for faster agents.
Cost Reduction
Decrease operational costs through intelligent model routing, prompt compression, caching, and strategic use of smaller models for routine tasks.
Reliability & Error Handling
Implement robust retry logic, fallback strategies, timeout management, and graceful degradation to ensure agents remain reliable under varying conditions.
Multi-agent Coordination
Optimize agent-to-agent communication, task distribution, workflow orchestration, and consensus mechanisms for complex multi-agent systems.
RAG Pipeline Optimization
Enhance retrieval accuracy, chunk size tuning, embedding model selection, re-ranking strategies, and context window management for RAG applications.
Observability & Monitoring
Implement comprehensive logging, tracing, metrics collection, and performance dashboards to understand agent behavior and identify bottlenecks.
Optimisation techniques
How we improve agent performance
Battle-tested optimization strategies that deliver measurable improvements across latency, cost, accuracy, and reliability.
Prompt Engineering
Refine prompts for clarity, consistency, and performance. Few-shot examples, chain-of-thought, structured outputs, and instruction optimization.
Model Selection & Routing
Choose optimal models for each task. Route simple queries to fast models, complex reasoning to larger models, and balance cost vs. capability.
Caching & Memoization
Implement semantic caching for repeated queries, prompt prefix caching, and result memoization to reduce redundant API calls and costs.
Workflow Optimization
Streamline agent decision trees, reduce unnecessary tool calls, parallelize independent operations, and minimize sequential dependencies.
Context Management
Optimize context window usage with summarization, sliding windows, relevant context selection, and dynamic token budget allocation.
Tool & Function Calling
Optimize tool schemas for clarity, reduce tool overload, implement smart tool selection, and minimize function calling overhead.
Optimisation roadmap
Our systematic approach starts with measurement, delivers quick wins early, then tackles deeper optimizations with continuous validation and monitoring.
Baseline & analysis
Week 1- Agent performance profiling with latency, cost, and quality metrics
- Workflow analysis identifying bottlenecks and inefficiencies
- Observability instrumentation and logging infrastructure setup
Quick wins implementation
Week 2- Prompt refinements and instruction clarity improvements
- Caching layer implementation for repeated queries
- Model routing optimization for cost-sensitive operations
Deep optimisation
Week 3–4- RAG pipeline tuning with retrieval accuracy improvements
- Multi-agent coordination refinements and workflow optimization
- Advanced error handling and retry strategy implementation
Monitoring & iteration
Week 5- Performance dashboard and alerting configuration
- A/B testing framework for ongoing optimization
- Documentation and runbook creation for continued improvements
Benefits of agent optimisation
Systematic optimization delivers compound benefits—faster responses, lower costs, better reliability, and happier users.
Measurable improvements
Track concrete metrics: latency reductions, cost savings, error rate decreases, and quality score improvements through systematic optimization.
Production-ready patterns
Apply battle-tested techniques from production AI systems. No experimental approaches—only proven optimizations that work at scale.
Cost-efficiency focus
Reduce operational costs by 30-70% through intelligent caching, model routing, and prompt optimization without sacrificing quality.
Faster response times
Decrease agent latency through parallel processing, caching strategies, and efficient context management for better user experiences.
Increased reliability
Improve system uptime and resilience with robust error handling, fallback strategies, and graceful degradation under load.
Scalable architecture
Build agent systems that handle increasing load gracefully. Optimization patterns that maintain performance as usage grows.