Agentic Optimization: From Chaos to High Performance
Your agents worked in the demo. But in production, they stall, burn compute, and break unpredictably. We fix that.
Agentic Optimization is our professional solution that transforms tangled, unreliable agents into streamlined, stable, and cost-efficient systems.
"Optimize everything: prompts, context, compute. No guesswork, just results."
Why Agents Fail in Production
Unstable Performance
Agents succeed once, fail the next run
High Compute Costs
Cloud bills balloon with no clear ROI
Constant Prompt Rewrites
Manual fixes break other logic
No Performance Metrics
Hard to measure, harder to improve
The biggest cost in AI isn't compute • it's confusion and rework. We turn chaos into clarity.
Before vs After Optimization
Current State
- Rewriting prompts every week
- Agents break after updates
- Compute overuse and slow responses
- Manual QA testing cycles
- Confusing, unpredictable behavior
Optimized State
- Optimized prompt logic/pipelines
- Stable across models and vendors
- Right-sized infra with lower costs
- Automated evaluation and monitoring
- Transparent, predictable performance
What We Optimize
We improve performance across the three critical layers of your AI agents:
Prompt Optimization
Stabilize agent behavior with modular, reusable prompts that work across models.
- Convert prompts into example-based, modular components
- Stabilize performance across GPT, Claude, open models
- Remove 'prompt spaghetti' and simplify logic
Context Optimization
Engineer context delivery so agents use the right data at the right time for accurate results.
- Design optimal context retrieval and delivery
- Create structured datasets for evaluation
- Introduce data-first thinking to reduce trial/error
- Optimize RAG and knowledge base integration
Compute Optimization
Streamline inference and infrastructure to cut costs and reduce latency without sacrificing performance.
- Evaluate and benchmark inference layers: Ollama, SGLang, TGI, etc.
- Model strategy: balance latency vs cost vs quality
- Analyze real GPU/CPU usage and optimize for load
- Right-size infrastructure for actual workloads
Ready to Optimize Your Agents?
Select the optimization area that matches your biggest performance challenges and email us to begin.
Click your preferred optimization below to send an email to optimization@super-agentic.ai. We'll respond within 24 hours to analyze your agents and start optimization.
Prompt Optimization
2–3 weeks delivery
Transform your prompts into stable, modular components
Includes:
- Convert prompts to modular, reusable components
- Stabilize performance across all major models
- Optimize prompts with best Optimizers e.g DSPy
- Create evaluation harnesses with golden examples
- Team training on prompt engineering best practices
Deliverables:
Context Optimization
3–4 weeks delivery
Engineer perfect context delivery for accurate results
Includes:
- Optimize RAG pipelines and retrieval systems
- Design structured datasets for evaluation
- Implement context engineering best practices
- Context Enginering Tools and Techniques
- Build golden examples and eval suites
Deliverables:
Compute Optimization
3–5 weeks delivery
Cut costs and reduce latency without sacrificing quality
Includes:
- Benchmark and optimize inference infrastructure and tools
- Right size compute resources for actual workloads
- Implement model strategy balancing cost vs performance
- Implement right tool Ollama, MLX, vLLM, SGLang or other suitable
- GPU/CPU usage analysis and optimization
Deliverables:
Custom Agent Optimization
4–8 weeks delivery
Complete end to end optimization across all agentic layers
Includes:
- All optimization areas combined
- Custom framework development
- Multi agent ecosystem optimization
- Advanced monitoring and alerting
- Dedicated optimization team and ongoing support
Deliverables:
Proven 5-Step Process
Systematic optimization methodology that delivers measurable results
System Diagnosis
Analyze prompts, context, infra, compute usage.
Optimization Tools Setup
Introduce proper tools and frameworks, eval harnesses, benchmarking/inference tools.
Iterative Experiments
Test across layers: prompts, context, models, latency.
Team Training or Implementation
We train your engineers or handle it directly.
Deliver Final Systems
Optimized agents, monitoring, ongoing roadmap.
Guaranteed Business Impact
Measurable improvements across performance, cost, and reliability
Consistent agent logic
Reliability across vendors/models
Reduced compute costs
Lower GPU/cloud bills, smarter infra usage
Safety-first systems
Fewer unknowns, more predictability
Evaluation-first ops
Measurable, traceable improvement
In-house knowledge
Teams that understand, not just deploy
Our Commitment
We work with you until your agents perform at their best.
How We Do It (technical details)
We use evaluation-first practices and the latest optimization frameworks, including:
Systematic eval harnesses with golden examples
Build comprehensive test suites with real-world scenarios using Synthetic data generation technique
Context engineering and retrieval tuning
Optimize RAG pipelines and context delivery mechanisms using suitable vectoDB and embedding models.
DSPy and self-optimization methods
Implement automated prompt optimization and model tuning either using wide range of DSPy optimizers or best approch for your use case.
Industry Standard Inference Engines
Implement best suited infernce engine for your use case Ollama, SGLang, vLLM, TGI, MLX etc
This ensures your team gets both working systems and the skills to maintain them.
Ready to Optimize Your Agents?
Stop wasting resources on unstable systems. Get measurable performance improvements and unlock the full potential of your AI investment.