Deloitte: Agentic AI Consultant

# Input data: Dict mapping category keys to lists of tuples (ID, Category, Value)
data = {
    "batch_1": [(101, "A", 45), (102, "B", 30), (103, "A", 80)],
    "batch_2": [(104, "B", 15), (105, "A", 60), (106, "C", 95)]
}

# Goal: Group all tuples across batches by their Category element (index 1)
grouped_data = {}

for batch in data.values():
    for item in batch:
        category = item[1]
        if category not in grouped_data:
            grouped_data[category] = []
        grouped_data[category].append(item)

print(grouped_data)

Output:

{
  'A': [(101, 'A', 45), (103, 'A', 80), (105, 'A', 60)],
  'B': [(102, 'B', 30), (104, 'B', 15)],
  'C': [(106, 'C', 95)]
}

1. User Query & Context Orchestration: The user query hits the agent framework (e.g., LangGraph, CrewAI). The system retrieves short-term conversation memory and relevant state history.
2. ReAct Reasoning (Thought Phase): The LLM evaluates the user intent against available tool descriptions (defined via JSON Schemas) and decides whether to output a direct response or execute a tool call.
3. Tool Execution (Action Phase): The framework invokes external functions (e.g., vector database retrieval, enterprise search, code interpreter, or external REST API).
4. Observation & Reflection (Evaluation Phase): The tool returns structured output back to the agent loop. The LLM verifies if the observation satisfies the goal. If error/incomplete, it retries or adjusts tool inputs.
5. Final Output Streaming: Once all sub-tasks complete, the final response is generated, passed through moderation/safety guardrails, and streamed back to the client.

• Approach 1: Human-in-the-Loop (HITL)

• Interrupt the agent execution prior to database call. Present the generated SQL query to an authorized human operator via an interface/prompt for manual verification and approval.

• Approach 2: Parameterized Template Search (Deterministic Pre-Approved SQL)

• Rather than permitting the LLM to generate raw arbitrary SQL strings, write pre-validated, parameterized SQL query templates stored inside a vector database.
• The agent uses semantic search to locate the correct query template and uses the LLM only to extract and populate entity parameters (e.g., date_range, user_id).

• Client-Led Engagements: When a client arrives with a mandatory pre-existing tech stack (e.g., Azure OpenAI, AWS Bedrock, LangChain, or Semantic Kernel), the consultancy builds custom agentic pipelines bound strictly within those enterprise constraints.
• Consultant-Led Engagements: When Deloitte retains full architectural ownership, the team selects the framework and stack based on project demands (e.g., choosing LangGraph for stateful multi-agent systems, AutoGen/CrewAI for collaborative agent roles, or custom Python orchestration using native APIs for low latency).

1. Identify the Bottleneck: Audit the sprint board to distinguish between technical blockers, scope creep, or missing external dependencies.
2. Re-Prioritize & De-Scope: Conduct a backlog grooming session with stakeholders to freeze non-essential feature additions and focus resources strictly on the Minimum Viable Product (MVP).
3. Resource Re-Allocation & Swarming: Pair senior developers with junior teammates on critical-path blocking tasks to clear execution hurdles quickly without introducing technical debt.
4. Transparent Stakeholder Communication: Proactively inform project managers and client leads with an updated burn-down chart, adjusted risk matrix, and clear mitigation timeline.