Complete Playbook

Agentic AI Builder's Framework

Build production-grade AI agents the right way: a complete framework with contextual memory and human oversight.

Six Phases of Agent Development

A complete roadmap from concept to production

The Foundation (Define the 'Why')

Establish business case and user-centric mission

1.1 Problem & Agent Value Proposition

Problem Statement: What specific, high-cost process are you automating?
Why an Agent?: Justify why this needs an agent vs. simple prompt or rule
Value Hypothesis: By building an agent that can [ACTION], we will help [PERSONA] achieve [OUTCOME], resulting in [MEASURABLE_KPI]

1.2 Define Personas & Interaction Models

Personas: Who is the user? (Support Engineer, Sales Analyst, Patient, etc.)
Interaction Model: Human-to-Agent (H2A) vs. Agent-to-Agent (A2A)
User Flows: Map the happy path and failure points

1.3 User Flows & Success Metrics

Define KPIs: 'Reduce ticket resolution time by 30%'
Map the happy path and key failure points

The Blueprint (System Architecture)

Design solution and select core tech stack

2.1 High-Level System Architecture

Components: UI/Client, API Gateway, Orchestrator (MCP Server), Agent Services
Tool/API Layer, Knowledge Base (Vector DB), Context & Memory Layer
Operational DB (SQL for users, chat history, logs)

2.2 Data & Knowledge Architecture

Operational DB (SQL): Schema for users, chat history, logs
Knowledge Base (Vector): Schema for static RAG data (Phase 3)
Context & Memory (Ledger/Vector): Schema for dynamic, user-specific data (Phase 4)

2.3 Select the AI Stack

Performance: GPT-4o, Claude 3 Opus (high-stakes reasoning)
Speed/Cost: Claude 3 Haiku, Llama 3 8B, Mistral-Nemo (routing)
Privacy (Self-Hosted): Llama 3, Mistral
Key Frameworks: LlamaIndex, LangChain/LangGraph, FastAPI

The Knowledge Core (Static Memory)

Build agent's public or domain memory (the 'R' in RAG)

3.1 Ingest

Build data connectors for domain knowledge (Confluence, websites, technical manuals)
Key Frameworks: LlamaIndex, Airbyte, Unstructured.io

3.2 Chunk - Decision Point

Semantic (SemanticSplitterNodeParser): Groups by topic, excellent for prose
Document-Aware (Markdown/Header): Best for structured docs, keeps sections intact
Agentic Chunking: LLM decides how to chunk as it reads (Advanced)

3.3 Index & Feature Enrichment

Vectorize: Convert chunks to embeddings (text-embedding-3-large, BGE)
Add Rich Metadata: doc_id, section_title, created_by, date, security_level
Vector Store: Load into Pinecone, Weaviate, or Postgres w/ pgvector

The Context & Memory Layer

Build persistent user-specific memory across all sessions and tools

4.1 Context Ingestion Framework (The 'Ears')

Enterprise Connectors: Slack (messages), Email (IMAP/Graph API), ServiceNow, Jira (tickets), Confluence (docs)
Application Connectors: Chat History, UI Clicks (navigation paths)
Agent Connectors: Tool Outputs (results), Inter-agent Messages (A2A workflows)

4.2 Context Ledger (The 'Raw Facts')

Purpose: Immutable, append-only log of all events (auditable ground truth)
Technology: Event stream (Kafka) or time-series database
Captures: timestamp, user_id, source, event_type, content_hash, metadata

4.3 Memory Synthesis Engine (The 'Processing')

Purpose: Turn raw, noisy Ledger into clean, usable Memory (backend A2A process)
Process: A 'Memory Agent' reads ledger, synthesizes it into insights
Example: 'User has reported 3 VPN issues this month via Slack and ServiceNow'

4.4 Memory Store (The 'Retrievable Past')

Purpose: Separate vector store for synthesized memories from 4.3
Critical Metadata: user_id, topic, date_range, source_system (slack, email, etc.)
This is the agent's 'personal' memory (distinct from Knowledge Core)

4.5 Contextual Governance & Privacy (The 'Gate')

Purpose: Enforce configurable data access per user, per agent, per data source
Context Policy Service: Before retrieval, check consent and permissions
Example: 'Does user-123 consent to prescription history for billing questions?'

The Agentic Core (Reasoning & Orchestration)

Build agents using Static Knowledge + Personal Memory

5.1 The 'Brain' (Reasoning Loop)

ReAct Pattern: Reason → Act → Observe → Repeat
Super-charged Observe: Retrieves from BOTH Knowledge Core (Phase 3) AND Memory Store (Phase 4)
LLM for reasoning + Tools for acting = Agent Loop

5.2 The 'Orchestrator' (Collaboration)

MCP Server: Central hub managing state and routing tasks (H2A entrypoint)
MCP Client(s): Individual agents as microservices registering their tools (A2A workers)

5.3 The 'Hands' (Tools)

Tools are annotated Python functions
CRITICAL NEW TOOL: retrieve_user_memory(query, sources=['all']) → checks Context Policy → queries Memory Store → returns memories
Other tools: search_knowledge_base(), get_user_profile(), request_human_review()

The Production Stack (Eval Ops & Scaling)

Productize agent for scalability, reliability, continuous improvement

6.1 Deploy as a Service

Containerize Agent Orchestrator (MCP Server) as microservice
Containerize each Agent (MCP Client) as separate microservice
Scale agents independently based on demand

6.2 Caching

Implement semantic caching before costly agent runs
Check if semantically similar query already answered

6.3 Eval Ops for Agents - Decision Point

Prompt Engineering (Fastest): Fix bad prompts using eval data
Fine-Tuning (Better): Use human annotation data to fine-tune model
Quantizing (Smaller/Faster): Reduce precision for speed with minimal quality loss
Feature Engineering: Add new valuable metadata to Knowledge Core and Memory Store

The Trust Layer (Evaluation & Governance)

Create human-in-the-loop system guaranteeing AI quality and reliability

7.1 Evaluation Methods - Decision Point

LLM-as-a-Judge: Use powerful LLM (GPT-4o) with clear rubric to score answers
Human Annotations (HITL): Build UI for experts to score/correct (ground truth)
Deterministic Evals: Code-based checks (fast, free, limited to simple rules)
NEW: Contextual Relevancy - Did agent use user history appropriately?
NEW: Contextual Completeness - Did agent miss obvious user history it should use?

7.2 Hybrid Confidence Scoring (Quality Gating)

Combine: Retrieval Score + LLM Self-Eval + LLM-as-Judge Score
Gate: score > 0.9? → Send to user : FLAG and send to HITL queue
NEVER send low-confidence answers to users

7.3 AI Observability (The Traces)

Capture entire agent workflow: Query → Thoughts → Memories Retrieved → Knowledge Retrieved → Tools → Result
Track: Cost, latency, tokens, which memories were used, which docs were retrieved
Frameworks: Langfuse, Arize, Weights & Biases

Critical Decision Points

Make informed choices at each phase

Interaction Model (Phase 1)

Human-to-Agent (H2A)

User gives task, agent completes it. Most common model (chatbots, assistants)

Agent-to-Agent (A2A)

One agent triggers another. For fully automated backend processes

LLM Selection (Phase 2)

🎯 Performance: GPT-4o, Claude 3 Opus

⚡ Speed: Claude 3 Haiku, Llama 3 8B

💰 Cost: Haiku, Llama 3 8B, Mistral-Nemo

🔒 Self-Hosted: Llama 3, Mistral

Chunking Strategy (Phase 3)

• Semantic: Groups by topic, best for prose

• Document-Aware: Best for structured docs

• Agentic: LLM decides chunks (advanced)

Memory Architecture (Phase 4)

• Connectors: Slack, Email, ServiceNow, Jira, Custom APIs

• Ledger: Immutable event log (Kafka/Time-Series DB)

• Synthesis: Backend Memory Agent processing

• Store: Vector DB for user-specific memories

• Governance: Context Policy Service for consent

Orchestration Pattern (Phase 5)

• MCP Server: Central orchestrator (H2A entrypoint)

• MCP Clients: Individual agents as microservices (A2A)

• retrieve_user_memory(): NEW critical tool for context

Eval Ops (Phase 6)

• Prompt Engineering: Fastest, use eval data

• Fine-Tuning: Better, use human annotations

• Feature Engineering: New metadata for Knowledge/Memory

Evaluation Methods (Phase 7)

• LLM-as-Judge: Fast, scalable, needs rubric

• Human Annotation: Ground truth, expensive

• Deterministic: Fast, free, simple checks only

NEW: Contextual Relevancy & Completeness metrics for memory usage

Hybrid Confidence Scoring

The Quality Gate: Ensuring only high-confidence answers reach users

The Flow

Generate Response + Score

Combine: Retrieval Score + LLM Self-Eval + LLM-as-Judge Score

Quality Gate Decision

IF score > 0.9 → Send to user

Flag Low Confidence

IF score < 0.9 → FLAG and send to Human-in-the-Loop queue

Human Review

Expert annotates and corrects, creating golden dataset

Key Principle

NEVER send low-confidence answers to users. Always gate with hybrid scoring + HITL.

The Continuous Improvement Loop

How Evals Feed Back Into the System

📋 Diagram Flow Guide:

Input Layer

Phase 3 & 4 feed knowledge and memories

Processing

Agent makes decisions

Output & Feedback

Quality gates and loops

graph TD subgraph Phase3_4["Phase 3 & 4: Memory"] A["Phase 3: Knowledge Core"] B["Phase 4: Context & Memory"] end subgraph Phase5["Phase 5: Agent Brain"] C["Agent Orchestrator"] end subgraph Phase6_7["Phase 6 & 7: Ops & Trust"] D("User Query") E["Quality Gate"] F["Send to User"] G["HITL Queue"] H["Human Annotation"] I["Golden Dataset"] J("Log Trace") K["AI Observability"] L["Analytics Dashboard"] M["Eval Ops Pipeline"] N["Fine-Tune Models"] O["Improve Prompts"] P["Update Knowledge"] end D -->|1| C A -->|2| C B -->|2| C C -->|3| E E -->|4: Yes| F E -->|5: No| G G -->|6| H H -->|7| I F -->|8| J G -->|8| J J -->|9| K K -->|10| L I -->|11| M L -->|11| M M -->|12| N M -->|12| O M -->|12| P N -->|13| C O -->|13| C P -->|13| A P -->|13| B style Phase3_4 fill:#e3f2fd,stroke:#1976d2,stroke-width:2px style Phase5 fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px style Phase6_7 fill:#fce4ec,stroke:#c2185b,stroke-width:2px

📊 The Complete Flow - Step by Step:

User Query Arrives

User sends a query to the system entry point

Knowledge Core (Phase 3)

Static docs retrieved from knowledge base and fed to agent

Context & Memory (Phase 4)

User history and personal memory retrieved and provided

Agent Processes Query (Phase 5)

MCP Orchestrator runs ReAct loop: Reason → Act → Observe using Phase 3 & 4 data

✓ High Confidence Path

If confidence score > 0.9 → Response goes to user

✗ Low Confidence Path

If confidence score < 0.9 → Route to HITL Queue

Human Annotation (Phase 7)

Expert reviews and corrects low-confidence responses

Build Golden Dataset

Human annotations create ground truth for training models

Log All Traces

Both paths log complete execution traces (query, thoughts, tools, result)

AI Observability (Phase 7)

Langfuse/Arize captures and visualizes all traces

Analytics Dashboard

Identify failure patterns and optimization opportunities

Eval Ops Pipeline (Phase 6)

Combines insights from golden dataset and analytics

Improve Prompts

Use eval data to refine agent prompts → Feeds back to Agent

Fine-Tune Models

Use golden dataset to fine-tune models → Feeds back to Agent

Update Knowledge & Memory

Use insights to improve Phase 3 (add metadata/docs) and Phase 4 (update memory)

The Eval Ops Feedback Loop

Production Agent

Serves user queries with confidence scoring

Quality Gate

Route low-confidence to HITL, high-confidence to user

Log Traces

Capture full workflow: query → reasoning → tools → result

AI Observability

Langfuse/Arize: Identify failures, cost, latency

Human Annotation (HITL)

Build golden dataset from expert corrections

Eval Ops Pipeline

Use insights to improve system

Continuous Improvement Actions

📝 Re-Train/Fine-Tune Models

Use golden dataset from HITL to fine-tune models

✍️ Improve Prompts

Use traces to identify and fix bad prompts

🧠 Update Knowledge Base

Add new metadata, improve chunking strategy

🔧 Add New Tools

Identify missing capabilities from failure patterns

How the Tech Stack Enables the Framework

Python

Foundation

• Async/await for scale

• Type hints for safety

• Custom eval logic

LlamaIndex

Phase 3: RAG

• Semantic chunking

• Rich metadata

• AutoMerging

LangChain

Phase 4: Tools

• Chain composition

• 200+ loaders

• Tool framework

LangGraph

Phase 4: Orchestration

• Graph workflows

• Checkpointing

• Multi-agent

Langfuse

Phase 6: Observability

• Trace collection

• Cost tracking

• A/B testing

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