Agents Companion: Architecture, Ops, and Evaluation

Agent Companion

An Agent is an application designed to achieve specific objectives by perceiving its environment and acting strategically using available tools. The core principle of an agent is its integration of reasoning, logic, and external information access—allowing it to make decisions beyond the base model’s capabilities. These agents operate autonomously, pursuing goals proactively and determining subsequent actions without step-by-step instructions.

🔧 AgentOps & GenAIOps Continuum

AgentOps concerns the operationalization of agents. It involves:

• Tool management (internal & external)
• Agent Brain Prompt: goal, profile, and instructions
• Orchestration and Memory management
• Task decomposition and execution flow

Core Capabilities:

• Version control, automated CI/CD deployments
• Unit/integration testing and logging
• Security, authentication, secret management
• Metrics, throttling, quotas, exception handling
• Scalability and privacy compliance

Tech stack evolution:

• DevOps is about operationalizing deterministic applications via people, process, and technology.
• MLOps extends DevOps to model-based, non-deterministic outputs powered by data.
• FMOps adds foundation model management and fine-tuning workflows.
• PromptOps handles prompt lineage, storage, templating, optimization, and evaluation.
• RAGOps manages retrieval pipelines: chunking, vectorization, ranking, and grounding.
• AgentOps orchestrates agents with memory, toolsets, goal-driven logic, and task routing.

High-fidelity Ops implementations also reflect organizational structure and customer workflow.

🎯 Success Metrics & Evaluation

Agent Success Metrics:

Evaluation Dimensions:

1. Agent Capabilities: Tool calling, reasoning, planning. Benchmarks like BFCL and τ-bench evaluate function calling and plan execution.
2. Trajectory Evaluation: Compares predicted tool-call sequences to ground truth. Metrics include:
   • Exact Match (strict)
   • Ordered Match (core steps in order, flexible extras)
   • Any Order (all steps regardless of sequence)
   • Precision, Recall, and Single-tool usage
3. Response Evaluation: A final output assessed by an auto-rater LLM acting as a judge, based on defined criteria.

For multi-agent systems, evaluate:

• Cooperation and coordination
• Planning and task assignment effectiveness
• Agent utilization and operational scaling

🧩 Multi-Agent Topologies & Roles

Common agent types:

• Planner Agents: Decompose high-level goals into structured sub-tasks
• Retriever Agents: Perform dynamic data fetching
• Execution Agents: Generate responses or invoke APIs
• Evaluator Agents: Validate output coherence and quality

Topology choices:

• Single agent, network, supervisor, hierarchical, or customized
• System architectures can be sequential, collaborative, competitive, or layered

Key components:

• Interactive wrapper, memory management (short + long term)
• Cognitive subsystem (CoT, ReAct, planning)
• Tool integration (registries), routing, delegation
• Feedback loops, reinforcement learning
• Agent-to-agent communication and persistence layers

🧪 Agentic RAG Workflow

Key steps before agent introduction:

• Document ingestion
• Metadata extraction, embeddings, vector DB setup
• Similarity search, re-ranking, grounding prompts

📋 Contract & Project Definition Checklist

Recommended tools: Google Agentspace, NotebookLM Enterprise, Vertex Eval, Vertex Search, Cloud Observability

📚 Resources & References

• Agentic Design Notes PDF
  Additional insights and original source breakdown on agent structure, evaluation, and deployment.