Autonomous Agent Architectures: A Comprehensive Analysis of the Task-to-Agent Paradigm Shift in Enterprise Automation

import numpy as np
from scipy.stats import dirichlet

class BeliefStateManager:
    def __init__(self, state_space_size: int, prior_alpha: float = 1.0):
        # Initialize with Dirichlet prior for computational efficiency
        self.belief = dirichlet.rvs(alpha=[prior_alpha] * state_space_size)[0]
        self.transition_model = self._initialize_transition_model()

    def update(self, action: int, observation: np.ndarray) -> np.ndarray:
        """Bayesian belief update using particle filtering"""
        # Predict step
        predicted_belief = self.transition_model[action] @ self.belief

        # Update step (Bayes rule)
        likelihood = self._observation_likelihood(observation)
        posterior = predicted_belief * likelihood

        # Normalize
        self.belief = posterior / np.sum(posterior)

        # Particle resampling for computational efficiency
        if self._effective_sample_size() < 0.5 * len(self.belief):
            self._resample_particles()

        return self.belief

    def _effective_sample_size(self) -> float:
        """Calculate ESS to detect particle degeneracy"""
        return 1.0 / np.sum(self.belief ** 2)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import torch.nn as nn
from typing import List, Tuple, Dict, Any

class ReActAgent(nn.Module):
    def __init__(self, model_name: str = "gpt-4", max_iterations: int = 10):
        super().__init__()
        self.llm = AutoModelForCausalLM.from_pretrained(model_name)
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.max_iterations = max_iterations

        # Hierarchical planning components
        self.high_level_planner = HighLevelPlanner(self.llm)
        self.low_level_executor = LowLevelExecutor()

        # Memory components
        self.working_memory = WorkingMemory(capacity=512)
        self.episodic_memory = EpisodicMemory(
            embedding_dim=768,
            max_episodes=10000
        )

    def forward(self, goal: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Execute ReAct loop with hierarchical planning"""
        # Generate high-level plan
        plan = self.high_level_planner.generate_plan(goal, context)

        trace = []
        for i in range(self.max_iterations):
            # Reasoning step
            thought = self._reason(goal, context, trace)
            trace.append(("thought", thought))

            # Check if goal achieved
            if self._is_goal_achieved(goal, context, thought):
                break

            # Action selection using MCTS
            action = self._select_action_mcts(thought, context, plan)
            trace.append(("action", action))

            # Execute action
            observation = self.low_level_executor.execute(action, context)
            trace.append(("observation", observation))

            # Update context and memory
            context = self._update_context(context, action, observation)
            self.working_memory.add(thought, action, observation)

            # Replan if necessary
            if self._should_replan(trace, plan):
                plan = self.high_level_planner.replan(goal, context, trace)

        # Store episode for future learning
        self.episodic_memory.store_episode(goal, trace, context)

        return {
            "success": self._is_goal_achieved(goal, context, trace[-1][1]),
            "trace": trace,
            "final_context": context,
            "iterations": i + 1
        }

    def _select_action_mcts(self, thought: str, context: Dict, plan: Plan) -> Action:
        """Monte Carlo Tree Search for action selection"""
        root = MCTSNode(state=(thought, context), plan=plan)

        for _ in range(self.mcts_iterations):
            node = root

            # Selection (UCB1)
            while not node.is_leaf() and not node.is_terminal():
                node = node.select_child_ucb1(c=1.414)

            # Expansion
            if not node.is_terminal() and node.visits > 0:
                node.expand(self._get_valid_actions(node.state))

            # Simulation (rollout)
            reward = self._simulate_rollout(node.state, plan)

            # Backpropagation
            node.backpropagate(reward)

        # Return most visited action
        return root.get_best_action()

class HierarchicalTaskDecomposer(nn.Module):
    def __init__(self, d_model: int = 768, n_heads: int = 12, n_layers: int = 6):
        super().__init__()

        # Transformer encoder for goal understanding
        self.encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(
                d_model=d_model,
                nhead=n_heads,
                dim_feedforward=3072,
                dropout=0.1,
                activation='gelu',
                batch_first=True
            ),
            num_layers=n_layers
        )

        # Graph neural network for task dependency modeling
        self.task_gnn = TaskDependencyGNN(
            node_features=d_model,
            edge_features=128,
            hidden_dim=512,
            num_layers=4
        )

        # Hierarchical attention for multi-level planning
        self.hierarchical_attention = HierarchicalAttention(
            d_model=d_model,
            max_depth=5
        )

    def decompose(self, goal_embedding: torch.Tensor) -> TaskHierarchy:
        # Encode goal
        encoded = self.encoder(goal_embedding)

        # Generate task nodes
        task_nodes = self._generate_task_nodes(encoded)

        # Model dependencies
        task_graph = self.task_gnn(task_nodes)

        # Apply hierarchical attention
        hierarchy = self.hierarchical_attention(task_graph)

        return TaskHierarchy(hierarchy)

    def _generate_task_nodes(self, encoded: torch.Tensor) -> List[TaskNode]:
        """Generate task nodes using beam search with diversity penalty"""
        beam_size = 10
        diversity_penalty = 0.5

        # Implementation details omitted for brevity
        pass

import ray
from ray import serve
import asyncio
from typing import AsyncIterator

@ray.remote
class DistributedAgentOrchestrator:
    def __init__(self, num_agents: int = 100):
        self.agents = [AgentActor.remote() for _ in range(num_agents)]
        self.load_balancer = ConsistentHashLoadBalancer(self.agents)
        self.consensus_protocol = RaftConsensus(self.agents)

    async def execute_goal(self, goal: Goal) -> AsyncIterator[AgentUpdate]:
        """Execute goal with distributed consensus"""
        # Decompose into subtasks
        subtasks = await self._decompose_goal(goal)

        # Distributed task allocation using auction protocol
        allocations = await self._allocate_tasks_auction(subtasks)

        # Execute with consensus checkpoints
        async for update in self._execute_with_consensus(allocations):
            yield update

    async def _allocate_tasks_auction(self, tasks: List[Task]) -> Dict[Task, AgentActor]:
        """Combinatorial auction for optimal task allocation"""
        # Each agent bids on task bundles
        bids = await asyncio.gather(*[
            agent.generate_bids.remote(tasks) for agent in self.agents
        ])

        # Solve winner determination problem (NP-hard)
        # Using approximation algorithm with 2-approximation guarantee
        allocations = self._solve_wdp_approximate(bids)

        return allocations

    async def _execute_with_consensus(self, allocations: Dict[Task, AgentActor]):
        """Execute tasks with Byzantine fault tolerance"""
        execution_futures = []

        for task, agent in allocations.items():
            future = agent.execute_task.remote(task)
            execution_futures.append((task, future))

        # Stream results with consensus validation
        while execution_futures:
            # Wait for any agent to complete
            ready, execution_futures = ray.wait(
                [f[1] for f in execution_futures], 
                num_returns=1
            )

            task, result = await ready[0]

            # Validate result through consensus
            if await self.consensus_protocol.validate(result):
                yield AgentUpdate(task=task, result=result)
            else:
                # Re-allocate failed task
                new_agent = self.load_balancer.get_next_agent()
                retry_future = new_agent.execute_task.remote(task)
                execution_futures.append((task, retry_future))

class MixtureOfExpertsAgent(nn.Module):
    def __init__(self, num_experts: int = 8, d_model: int = 768):
        super().__init__()
        self.experts = nn.ModuleList([
            ExpertAgent(specialization=i) for i in range(num_experts)
        ])
        self.router = nn.Sequential(
            nn.Linear(d_model, 256),
            nn.ReLU(),
            nn.Linear(256, num_experts),
            nn.Softmax(dim=-1)
        )

    def forward(self, goal_embedding: torch.Tensor) -> torch.Tensor:
        # Route to top-k experts
        routing_weights = self.router(goal_embedding)
        top_k = 2

        expert_outputs = []
        selected_experts, weights = torch.topk(routing_weights, top_k)

        for idx, weight in zip(selected_experts, weights):
            expert_output = self.experts[idx](goal_embedding)
            expert_outputs.append(weight * expert_output)

        return sum(expert_outputs)

import learn2learn as l2l

class MAMLAgent(nn.Module):
    """Model-Agnostic Meta-Learning for quick adaptation to new domains"""
    def __init__(self, base_model: nn.Module, lr_inner: float = 0.01):
        super().__init__()
        self.base_model = base_model
        self.lr_inner = lr_inner

    def adapt(self, support_set: List[Tuple[Goal, Trace]], num_steps: int = 5):
        """Adapt to new domain using few examples"""
        adapted_model = l2l.clone_module(self.base_model)
        optimizer = torch.optim.SGD(adapted_model.parameters(), lr=self.lr_inner)

        for _ in range(num_steps):
            loss = 0
            for goal, trace in support_set:
                prediction = adapted_model(goal)
                loss += self.compute_loss(prediction, trace)

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

        return adapted_model

from pyDatalog import pyDatalog

class NeurosymbolicAgent:
    def __init__(self):
        self.neural_component = TransformerAgent()
        self.symbolic_reasoner = self._init_symbolic_reasoner()

    def _init_symbolic_reasoner(self):
        """Initialize Datalog-based symbolic reasoning"""
        pyDatalog.create_terms('can_execute, requires, provides, goal_achievable')

        # Define rules
        can_execute(Agent, Action) <= requires(Action, Capability) & provides(Agent, Capability)
        goal_achievable(Goal) <= can_execute(Agent, Action) & achieves(Action, Goal)

        return pyDatalog

    def reason(self, goal: str, context: Dict) -> Plan:
        # Neural component generates candidate actions
        neural_candidates = self.neural_component.generate_actions(goal, context)

        # Symbolic reasoner validates and refines
        valid_plans = []
        for candidate in neural_candidates:
            if self._symbolically_valid(candidate, context):
                valid_plans.append(candidate)

        # Neural component ranks valid plans
        return self.neural_component.rank_plans(valid_plans, goal, context)[0]