LLM Limits in Performance, Complexity, or Cost? Transform them into Multi-Agent Success at any Scale.

Discover your ideal LLMs →

Capture comprehensive and personalised Knowledge Graph (KG) of your organisation including:

• Classes
• Instances
• Events
• Data Products
• Demand- Resources
• Agents

Select and Prirotise Use Cases based on knowledge captured and llm cost-value analysis

• Define problems, autonomy for Agents
• Define KPIs: accuracy, task success, cost efficiency, latency, safety, etc.

Plan you multi-agents for your ideal LLMs →

Model & Framework Selection
• Foundation model: OpenAI GPT-4/5, Claude, Mistral, Gemini, Llama, etc.
• Agent Framework:LangChain, LlamaIndex, CrewAI , AutoGen, OpenDevin, Semantic Kernel, etc.
• Hosting: API vs. self-hosted (vLLM, Ollama, etc.)

Prompt Engineering & Agent design
• System and role prompts
• Task decomposition logic
• Define tools / actions  functions, APIs, search, database queries
• Prompt templates, chains, or planner–executor patterns

Tools & Functions
• Register tools / functions: APIs , data retrieval, summarization, External connectors
• Define observation–action loop:  ReAct
• Integrate retrieval augmentation  RAG
• Optionally add multi-agent coordination:  manager–worker or peer networks

Deployment & Infrastructure set up
• Deployment mode API backend, microservice, or containerized agent
• Queueing / event-driven orchestration Kafka, Celery, etc.
• Vector DBs Pinecone, Weaviate, Milvus, FAISS,etc.
• Add guardrails OpenAI moderation, NeMo Guardrails, Guardrails AI, Rate limiting, caching, token usage

In Production / amplifing multiple LLMs Ops with multi-agents→

Monitoring & Evaluation
• Operational metrics latency, token cost, throughput
• Quality metrics success rate, factuality, hallucinations
• Observability tools LangSmith, Weights & Biases, HoneyHive, Traceloop
• Collect logs and traces (inputs, prompts, outputs)
• Human-in-the-loop validation for sensitive tasks

Continuous improvement & Fine-Tuning
• Apply feedback loops (user or automated scoring)
• Tune prompts or tool configurations
• Refine training / RAG data
• Optionally perform LoRA / PEFT fine-tuning
• Conduct A/B testing for prompt or model variants

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Governance, Security and Compliance
• Enforce data privacy and encryption
• Manage access control (who can call which agent/tools)
• Log all LLM actions for auditability
• Ensure regulatory compliance (GDPR, HIPAA, SOC2)
• Detect and mitigate prompt injection / jailbreaks