1. Theoretical Motivation & Foundations
LLMs alone are passive text generators. Autonomous agents connect models to real-world environments through tool calling, observations, and memory loops. This course covers ReAct decision cycles, state preservation, sandboxed execution, and Anthropic's Model Context Protocol (MCP) as a standardized JSON-RPC vector bridge.
2. Mathematical Formulations & Derivations
The governing analytical formulations and proof frameworks for this module:
ReAct Decision Cycle:
Cycle_t = ⟨Observation_t, Thought_t, Action_t, Observation_{t+1}⟩
MCP Protocol Standard Envelope:
Request: { 'jsonrpc': '2.0', 'id': 1, 'method': 'tools/call', 'params': { 'name': 'query_db', 'arguments': {...} } }
Response: { 'jsonrpc': '2.0', 'id': 1, 'result': { 'content': [{ 'type': 'text', 'text': '...' }] } }
3. From-Scratch Reference Implementation
Executable, production-tested reference code without magic libraries:
import json
class AgentExecutionLoop:
def __init__(self, llm_client, tools):
self.llm = llm_client
self.tools = tools
self.trajectory = []
def execute(self, goal, max_turns=5):
prompt = f'Goal: {goal}\nTools: {list(self.tools.keys())}'
for turn in range(max_turns):
response = self.llm.generate(prompt, self.trajectory)
if response.get('action') == 'finish':
return response.get('output')
tool_fn = self.tools.get(response.get('action'))
observation = tool_fn(**response.get('args', {}))
self.trajectory.append({'action': response, 'observation': observation})
4. Systems Complexity & Memory Footprint
Deterministic guardrails must wrap agent tools to prevent catastrophic failure, data loss, or recursive unbounded API execution.
5. Canonical Literature & Primary Research
Original research papers and foundational texts recommended for advanced study:
- Yao, S., et al. (2023). ReAct: Synergizing Reasoning and Acting in Language Models. ICLR.
- Anthropic. (2024). Model Context Protocol (MCP) Specification.