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AI Architect: ROI-Driven System-2 Reasoning

Build AI systems that think before they act. Master Tree of Thoughts, Monte Carlo Tree Search, verification reward models, and dynamic ROI guardrails — then deploy a financial reasoning agent that spends compute only where it pays off.

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4 weeks

What You'll Learn

Implement Tree of Thoughts and MCTS for structured multi-step reasoning
Scale test-time compute dynamically based on problem complexity and ROI
Build verification reward models that score candidate solutions without human labels
Implement automated critique loops and multi-agent debate for self-correction
Deploy a financial reasoning agent with dynamic compute budgets and audit trails

Course Content

W1
Week 1: System-2 Thinking Frameworks
Move from fast pattern matching to deliberate, structured reasoning.
1
System-1 vs System-2
Map Kahneman's dual-process theory onto LLM behavior and understand when fast inference fails and slow deliberation is required.
2
Tree of Thoughts (ToT)
Implement ToT to generate, evaluate, and prune multiple reasoning branches before committing to a final answer.
3
Handling Uncertainty
Quantify epistemic and aleatoric uncertainty in LLM reasoning and propagate it through multi-step decision trees.
4
Monte Carlo Tree Search (MCTS)
Apply MCTS to reasoning: selection, expansion, simulation, and backpropagation across a tree of candidate reasoning steps.
5
Policy-Guided Search
Use a learned policy to bias MCTS node selection toward higher-value reasoning paths, reducing the search space without sacrificing coverage.
Weekly Win
MCTS Reasoning Prototype
A working MCTS implementation that explores reasoning branches for a multi-step math or logic problem and selects the best solution path.
W2
Week 2: Test-Time Compute & ROI
Spend compute where it changes the answer, not where it doesn't.
1
Test-Time Compute Scaling
Analyze the empirical relationship between compute budget at inference time and final answer quality across task types.
2
Compute-Optimal Strategies
Apply compute-optimal scaling laws to decide when to generate more samples vs. when to run deeper search on fewer candidates.
3
Verification Reward Models
Train or prompt-engineer a verifier that scores candidate reasoning chains without requiring ground-truth labels.
4
Cost-Benefit Analysis
Build a cost model that estimates the expected value improvement per additional compute token for a given problem class.
5
Dynamic ROI Calculation
Implement a runtime ROI calculator that adjusts the compute budget mid-inference based on convergence signals and diminishing returns.
Weekly Win
Dynamic Compute Budget Controller
A controller that allocates more MCTS iterations to high-uncertainty problems and terminates early when the reward model converges.
W3
Week 3: Critique & Self-Reflection Systems
Build agents that catch their own mistakes before surfacing answers.
1
Fallacy of Pure LLM Logic
Document the systematic reasoning failures that emerge when LLMs reason without external grounding or verification steps.
2
Automated Critique Loops
Implement a critic agent that reviews reasoning chains and generates targeted improvement prompts fed back into the generator.
3
Contrastive Reflection
Generate competing solutions and use contrastive comparison to identify subtle errors invisible in single-solution evaluation.
4
Symbolic Integration
Offload exact computation to symbolic solvers (SymPy, Z3) and ground LLM reasoning in verifiable symbolic outputs.
5
Multi-Agent Debate
Run structured debate between agents assigned opposing positions to surface hidden assumptions and force explicit justification.
Weekly Win
Self-Correcting Reasoning Agent
An agent that critiques its own first-pass answer, runs contrastive reflection, and produces a measurably more accurate final output.
W4
Week 4: Capstone — Financial Reasoning Agent
Deploy a high-stakes reasoning agent with dynamic compute guardrails and a full audit trail.
1
Capstone: The Financial Benchmark
Define a financial reasoning benchmark — multi-step portfolio analysis, risk calculation, or regulatory compliance — with ground-truth answers.
2
Capstone: Action Space Definition
Specify the agent's action space: available tools, data sources, intermediate reasoning steps, and terminal answer format.
3
Capstone: MCTS Agent Coding
Implement the full MCTS reasoning loop with policy-guided selection, the verification reward model, and symbolic grounding.
4
Capstone: Dynamic ROI Guardrails
Wire the ROI calculator into the MCTS loop so the agent terminates automatically when further compute is not cost-justified.
5
Capstone: Execution and Audit
Run the agent on the benchmark, log every reasoning step to an immutable audit trail, and compare against a baseline chain-of-thought agent.
Weekly Win
Deployed Financial Reasoning Agent
A live MCTS agent that outperforms chain-of-thought on the financial benchmark while spending compute dynamically and producing an audit log.

Prerequisites

Python and LLM API experience
Basic probability and statistics
Familiarity with agent frameworks

Hands-on Project

Build an MCTS-powered financial analysis agent that allocates test-time compute dynamically, validates reasoning with a reward model, and logs a full decision audit trail.

📚
Advanced Level
Course Price
₹14,999
India
$249
International · One-time payment
Next cohort starts Mar 30
Duration4 weeks
LevelAdvanced
FormatCohort-based
Modules4

What's included:

Live cohort sessions
Hands-on projects
Certificate of completion
Lifetime access
Career support

Part of Learning Track

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AI Architect
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