Static multi-stage recommendation pipelines have hit a structural ceiling. The DAG-based retrieval-ranking-reranking model served production systems well when user behavior shifted slowly and retraining cycles measured in days were acceptable. Neither condition holds in 2026. This article maps the precise architectural decisions required to migrate from static execution graphs to an Agentic Recommender System (AgenticRS) framework, with explicit treatment of the functional closure criteria, orchestration logic, and reward alignment mechanisms that determine whether the transition succeeds or collapses under its own complexity.
Deconstructing the Static Pipeline Paradigm
Traditional DAG-based recommender systems experience a 15–20% degradation in CTR when user intent shifts faster than model retraining windows. The architectural reason is not slow models — it's the absence of feedback paths within the execution graph itself. A DAG moves data forward; it cannot adapt its own topology in response to observed outcomes.
The canonical multi-stage pipeline — candidate generation → scoring → filtering → reranking — executes each stage as a stateless function call. Each module consumes upstream output and emits downstream input. No stage owns its own evaluation criteria or correction loop. When the retrieval stage starts serving stale candidates, it has no mechanism to detect or self-correct that condition. The reranker silently compensates until the entire system drifts far enough from reality that a human engineer triggers a retraining job.
Technical Warning: In MLOps contexts, this silent drift is the most expensive failure mode. Monitoring reranker score distributions in isolation will not surface upstream retrieval quality collapse.
The shift to an AgenticRS fundamentally reorganizes this dependency chain. Modules that previously passed tensors now pass structured messages. Modules that previously ran-and-forgot now maintain feedback loops. The architectural contrast is direct:
graph TD
subgraph Static DAG Pipeline
A[User Request] --> B[Candidate Retrieval]
B --> C[Scoring Model]
C --> D[Business Filter]
D --> E[Reranker]
E --> F[Response]
end
subgraph AgenticRS Loop Architecture
G[User Context] --> H{Orchestrator}
H --> I[Retrieval Agent]
H --> J[Ranking Agent]
H --> K[Filtering Agent]
I -->|State + Reward Signal| L[Inner Reward Bus]
J -->|State + Reward Signal| L
K -->|State + Reward Signal| L
L --> M[Global Reward Evaluator]
M -->|Policy Update| I
M -->|Policy Update| J
M -->|Policy Update| K
M --> N[Response]
N -->|Outcome Feedback| M
end
The critical difference is the feedback arc from outcome back to each agent's policy. Static DAGs lack dynamic rerouting capabilities — once the graph is compiled, topology is fixed until the next deployment. AgenticRS replaces compiled topology with runtime negotiation between agents and an orchestrator that persists state across requests.
"The shift from static to agentic isn't just architectural; it's a transition from task execution to continuous optimization loops."
This distinction has direct MLOps consequences: deployment units change from monolithic pipeline artifacts to versioned agent policies, each with independent rollout, evaluation, and rollback semantics.
Defining Functional Closure in AgenticRS
Not every module in a recommendation pipeline qualifies for agent promotion. Promoting a module prematurely — before it satisfies closure criteria — creates a pseudo-agent that consumes orchestration overhead without delivering autonomous value. The AgenticRS paper establishes three hard requirements for functional closure.
A module is classified as functionally closed if it satisfies all of the following conditions simultaneously:
from dataclasses import dataclass
from typing import Callable, Any
import numpy as np
@dataclass
class AgentClosureCriteria:
"""
Formal criteria for promoting a pipeline module to an autonomous agent.
All three conditions must evaluate to True before promotion.
"""
module_id: str
eval_fn: Callable[[Any], float] # Independent evaluation function
decision_space: list[str] # Enumerable set of evolvable actions
feedback_loop_fn: Callable[[Any], Any] # Closed feedback mechanism
def is_independently_evaluable(self, test_inputs: list[Any], threshold: float = 0.85) -> bool:
"""
Module must achieve P(success) > 0.85 across 10,000 simulated
iterations without modification from downstream modules.
"""
results = [self.eval_fn(inp) for inp in test_inputs]
success_rate = np.mean([r >= threshold for r in results])
return success_rate > threshold
def has_evolvable_decision_space(self) -> bool:
"""
Decision space must be non-singleton — the agent must have
meaningful policy choices, not just a single execution path.
"""
return len(self.decision_space) > 1
def forms_closed_feedback_loop(self, state: Any) -> bool:
"""
The module must be able to consume its own output as input
to a corrective signal without requiring external orchestration.
"""
corrected_state = self.feedback_loop_fn(state)
# A closed loop is confirmed when the feedback function returns
# a state that differs from input — evidence of self-correction capacity.
return corrected_state != state
def satisfies_closure(self, test_inputs: list[Any], state: Any) -> bool:
"""Promotion gate: ALL three criteria must pass."""
return (
self.is_independently_evaluable(test_inputs)
and self.has_evolvable_decision_space()
and self.forms_closed_feedback_loop(state)
)
Pro-Tip: Run the
is_independently_evaluablecheck against a held-out behavioral simulation, not production traffic. Production inputs contain distribution artifacts from prior pipeline stages that will falsely inflate closure scores.
The closure check prevents a common failure pattern: wrapping a stateless embedding lookup in an "agent" interface and calling it agentic. If the module cannot self-evaluate, cannot choose between decision variants, and cannot produce a corrective signal from its own output, it remains a function — and should stay a function. Functional closure requires that a module can be independently evaluated, possess an evolvable decision space, and operate as a closed feedback loop.
Self-Evolving Modules as Independent Agents
An agent's evolvable decision space defines the surface area available for autonomous improvement. In enterprise AI deployments, three categories of agents emerge from standard DLRM component decomposition, each with distinct evolution axes:
| Agent Type | Evolution Axis | Primary Metric | Secondary Metric | Evolution Mechanism |
|---|---|---|---|---|
| Model Agent | Architecture search, weight update strategy | Validation AUC delta per epoch | Inference latency (p99) | Offline RL over hyperparameter action space |
| Feature Agent | Feature set selection, embedding dimension | Feature importance Shapley score | Feature freshness lag (ms) | Bandit over feature subset combinations |
| Resource Agent | Compute allocation, batching strategy | Cost per 1k inferences ($) | Throughput (QPS) | Policy gradient over resource allocation vectors |
| Retrieval Agent | Index selection, query expansion | Recall@K on held-out set | ANN latency (p50/p99) | A/B gated exploration with Thompson sampling |
| Ranking Agent | Model selection, ensemble weights | NDCG@10 | Score calibration error | Contextual bandit with user-segment stratification |
The distinguishing constraint for enterprise AI production systems is that evolution must be bounded. Unbounded exploration in live serving degrades user experience. Each agent's decision space requires both an exploration budget (max policy deviation per epoch) and a reversion trigger (metric threshold below which the prior policy is restored automatically).
Technical Warning: Resource agents are the highest-risk evolution target. A policy that over-allocates GPU memory to one agent starves adjacent agents, causing cascade failures that appear unrelated to the resource agent's own metrics.
The Orchestration Layer: Beyond Simple Execution
The orchestrator in an AgenticRS is not a scheduler. It is a stateful coordinator that maintains the current belief state of each agent, routes inter-agent messages, and enforces global constraints that individual agents cannot observe. The implementation challenge is persisting this state efficiently across the latency budget of a real-time recommendation request.
LangGraph provides a graph-based state machine model that maps directly to this requirement. Each node in the graph corresponds to an agent; edges carry typed messages; the graph state object persists agent beliefs across invocation cycles.
from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated
import operator
# --- Shared Agent State Schema ---
class AgenticRSState(TypedDict):
user_context: dict # Raw user features + session state
retrieval_candidates: list[str] # Item IDs from retrieval agent
ranked_candidates: list[dict] # Scored items from ranking agent
filtered_candidates: list[dict] # Post-constraint items
retrieval_reward: float # Local reward for retrieval agent
ranking_reward: float # Local reward for ranking agent
global_reward: float # System-level outcome signal
# Accumulate agent messages for audit and reward computation
agent_messages: Annotated[list[str], operator.add]
# --- Individual Agent Node Functions ---
def retrieval_agent(state: AgenticRSState) -> AgenticRSState:
"""
Retrieval agent: selects candidate set using its current policy.
Policy is keyed by agent version — enables hot-swap without graph restart.
"""
user_ctx = state["user_context"]
# In production, this calls a versioned ANN index + policy model
candidates = run_retrieval_policy(user_ctx, policy_version="v2.3.1")
local_reward = evaluate_retrieval_recall(candidates, user_ctx)
return {
**state,
"retrieval_candidates": candidates,
"retrieval_reward": local_reward,
"agent_messages": [f"retrieval_agent: recall={local_reward:.3f}"],
}
def ranking_agent(state: AgenticRSState) -> AgenticRSState:
"""
Ranking agent: scores candidate set. Reward computed against
user engagement labels from prior session — not downstream agent output.
This enforces independent evaluation per closure criteria.
"""
scored = run_ranking_policy(
state["retrieval_candidates"],
state["user_context"],
policy_version="v1.8.4",
)
local_reward = evaluate_ranking_ndcg(scored, state["user_context"])
return {
**state,
"ranked_candidates": scored,
"ranking_reward": local_reward,
"agent_messages": [f"ranking_agent: ndcg={local_reward:.3f}"],
}
def global_reward_node(state: AgenticRSState) -> AgenticRSState:
"""
Computes system-level reward and writes it back to state for
the outer reward loop. Alpha is dynamically adjusted per system
volatility — see reward alignment section.
"""
alpha = get_current_alpha(volatility_metric="session_entropy")
r_total = (alpha * state["retrieval_reward"]) + \
((1 - alpha) * state["ranking_reward"])
return {**state, "global_reward": r_total}
# --- Build the Agentic Graph ---
def build_agentic_rs_graph() -> StateGraph:
graph = StateGraph(AgenticRSState)
graph.add_node("retrieval", retrieval_agent)
graph.add_node("ranking", ranking_agent)
graph.add_node("global_reward", global_reward_node)
graph.set_entry_point("retrieval")
graph.add_edge("retrieval", "ranking")
graph.add_edge("ranking", "global_reward")
graph.add_edge("global_reward", END)
return graph.compile()
# Placeholder stubs — replace with actual policy dispatch logic
def run_retrieval_policy(ctx, policy_version): return []
def run_ranking_policy(candidates, ctx, policy_version): return []
def evaluate_retrieval_recall(candidates, ctx): return 0.9
def evaluate_ranking_ndcg(scored, ctx): return 0.85
def get_current_alpha(volatility_metric): return 0.6
The actor-model pattern here is deliberate: each agent function is a pure state transformer. Agentic Systems built on immutable state transitions are far easier to audit and replay for offline RL training than systems where agents mutate shared mutable objects.
High-Throughput Communication Infrastructure
The message bus connecting agents is not a convenience — it is the backbone of the system's temporal consistency guarantees. Sub-millisecond latency is mandatory for inter-agent state synchronization; at higher latencies, agents operating in parallel will make decisions on stale state, which is structurally identical to the drift problem present in static DAGs.
Kafka vs. Redpanda for Agentic Message Bus:
Redpanda demonstrates up to 25% lower tail latency compared to Kafka on identical 16-core NVMe hardware, attributable to its thread-per-core C++ execution model that eliminates JVM garbage collection pauses. Kafka remains the industry standard for sustained throughput above 500k messages/second, but JVM GC overhead creates unpredictable spikes at the 99.9th percentile — precisely the latency bracket that inter-agent state synchronization cannot tolerate.
"For agentic communication, predictability of latency is more critical than peak raw throughput."
The practical recommendation for enterprise AI deployments: use Redpanda for the inter-agent state synchronization bus (low-volume, latency-critical) and retain Kafka for high-volume telemetry and training data ingestion (high-volume, latency-tolerant). This dual-bus architecture isolates the latency-critical path from GC interference.
Asynchronous Consistency Architecture:
[Agent A] ──publish──► [Redpanda: agent-state-topic]
│
┌─────────▼──────────┐
│ Orchestrator │
│ State Aggregator │ ◄── compacted log (last-known-good state per agent)
└─────────┬──────────┘
│ broadcast
┌──────────────┼──────────────┐
▼ ▼ ▼
[Agent B] [Agent C] [Reward Node]
Each agent publishes state deltas, not full state objects. The orchestrator maintains a compacted projection of current agent states, which it broadcasts on each tick. Agents read only the most recent compacted snapshot, preventing message queue depth from growing unboundedly under load spikes.
Solving Multi-Agent Reward Misalignment
Reward misalignment is the primary production failure mode in multi-agent recommender deployments. Individual agents optimizing local metrics — retrieval recall, ranking NDCG, click-through rate — will converge on policies that maximize their own signal while degrading global user utility. A retrieval agent that maximizes recall by flooding the candidate set with popular items satisfies its local metric while destroying diversity downstream.
The layered inner/outer reward design couples local and global optimization through a single formula:
$$R_{\text{total}} = \alpha \cdot R_{\text{local}}(\text{agent}) + (1 - \alpha) \cdot R_{\text{global}}(\text{system})$$
Where: - $R_{\text{local}}$ is the agent's own closure-validated metric (e.g., recall@K for retrieval, NDCG@10 for ranking) - $R_{\text{global}}$ is the system-level outcome signal (e.g., session length, downstream conversion, diversity score) - $\alpha \in [0, 1]$ is dynamically adjusted based on system volatility metrics — specifically, the entropy of the current session distribution
When system volatility is low (stable user behavior), $\alpha$ increases, allowing agents more latitude to optimize locally. When volatility is high (distribution shift, new user cohort, product catalog update), $\alpha$ decreases, pulling each agent's policy closer to the global objective. This prevents individual agents from over-fitting to local signals during periods of environmental non-stationarity.
Pro-Tip: The $\alpha$ schedule is itself a control surface. Treat it as a hyperparameter with its own monitoring — a stuck-high $\alpha$ during a catalog shift is as dangerous as reward hacking.
Aligning individual agent objectives with global system utility prevents reward hacking and ensures systemic convergence. The mathematical binding between local and global rewards is what makes this more than a design principle — it is a trainable constraint.
Detecting Phase Transitions in Agent Behavior
Reward hacking and inter-agent collusion produce detectable statistical signatures before they cause visible business metric degradation. MLOps monitoring must target these leading indicators, not lagging business KPIs.
Threshold Signals Indicating Reward Hacking:
| Signal | Normal Range | Warning Threshold | Action |
|---|---|---|---|
| Agent policy entropy | 0.6–0.8 (normalized) | < 0.3 | Policy is collapsing to deterministic — exploration halted |
| Inter-agent message correlation | < 0.4 Pearson r | > 0.75 | Agents synchronizing on shared exploit — potential collusion |
| Local/global reward divergence | ΔR < 0.1 | ΔR > 0.25 per epoch | Local optimization decoupled from global objective |
| Candidate diversity (ILD) | > 0.55 | < 0.30 | Retrieval agent has collapsed to popularity bias |
Monitoring Implementation:
import numpy as np
from scipy.stats import entropy
class AgentDivergenceMonitor:
def __init__(self, divergence_threshold: float = 0.25, entropy_floor: float = 0.3):
self.divergence_threshold = divergence_threshold
self.entropy_floor = entropy_floor
# Ring buffer of recent reward pairs for trend detection
self.reward_history: list[tuple[float, float]] = []
def check_policy_entropy(self, action_distribution: np.ndarray) -> bool:
"""Returns True if agent policy has collapsed below safe entropy floor."""
# Normalize to probability distribution before entropy computation
probs = action_distribution / action_distribution.sum()
policy_entropy = entropy(probs)
normalized = policy_entropy / np.log(len(probs)) # normalize to [0,1]
return normalized < self.entropy_floor
def check_reward_divergence(self, r_local: float, r_global: float) -> bool:
"""Returns True if local/global reward gap exceeds safe operating threshold."""
self.reward_history.append((r_local, r_global))
if len(self.reward_history) < 10:
return False # Insufficient history for reliable divergence detection
recent = self.reward_history[-10:]
divergences = [abs(loc - glob) for loc, glob in recent]
return np.mean(divergences) > self.divergence_threshold
def check_inter_agent_collusion(self, agent_a_actions: list, agent_b_actions: list) -> bool:
"""Detects suspiciously high correlation between independent agent policies."""
if len(agent_a_actions) != len(agent_b_actions) or len(agent_a_actions) < 5:
return False
correlation = np.corrcoef(agent_a_actions, agent_b_actions)[0, 1]
return abs(correlation) > 0.75
Technical Warning: Phase transitions in agent behavior are non-linear. Systems will appear stable across hundreds of epochs, then shift abruptly. Monitoring only rolling averages will miss these transitions — maintain rolling standard deviation alongside mean for each metric.
Implementation Strategy: From DAG to Loop
Migration from a static DLRM pipeline to an AgenticRS is not a rewrite. It is a phased refactoring that preserves serving infrastructure while progressively adding closure properties to individual modules.
4-Step Checklist for Refactoring DLRM Components into Autonomous Agents:
Step 1 — Audit for Closure Eligibility
Run the AgentClosureCriteria.satisfies_closure() check against each DLRM module independently. Typical results: embedding lookup tables fail (no evolvable decision space), feature cross layers pass partially (evaluable, but feedback loop is implicit), interaction layers pass fully. Do not promote modules that fail — they remain static functions called by the orchestrator.
Step 2 — Instrument Independent Reward Signals Before adding agent infrastructure, attach a dedicated metric collector to each candidate module. Each module must demonstrate stable, independent measurement of its own performance across 10,000 simulation iterations without downstream input modification — the P(success) > 0.85 threshold. Modules that cannot produce a clean reward signal in isolation are not ready for agent promotion.
Step 3 — Replace Direct Calls with Message-Passing Interfaces Refactor module-to-module function calls into producer/consumer patterns on the message bus. This step is where Kafka/Redpanda integration occurs. Each module becomes a consumer of its input topic and a producer of its output topic. The orchestrator subscribes to all output topics and maintains the aggregated state projection.
Step 4 — Attach RL Policy Layer and Activate Feedback Loop Attach an offline RL policy (contextual bandit or policy gradient, depending on action space size) to each promoted agent. The policy consumes the local reward signal computed in Step 2 and updates the agent's decision parameters. Activate the feedback loop only after Steps 1–3 are stable in shadow mode — running the agent graph in parallel with the static DAG and comparing outputs before cutting over live traffic.
Pro-Tip: Run shadow mode for a minimum of 2 full business cycles (e.g., two weeks if weekly seasonality dominates) before live cutover. Agentic systems can over-fit to short-window behavior patterns that look correct in isolation.
Managing Model Storage and Self-Evolution
Persistent agent state and RL exploration history require a structured model store that supports versioned policy checkpoints, rollback, and concurrent reads from multiple agents. A flat model registry is insufficient — agents need to read not just current weights but prior policy trajectories for offline RL batch updates.
Modular Model Store Schema:
-- Agent policy versioning table
CREATE TABLE agent_policies (
agent_id VARCHAR(64) NOT NULL, -- e.g., "retrieval_agent_v2"
policy_version VARCHAR(32) NOT NULL, -- semver: "2.3.1"
artifact_uri TEXT NOT NULL, -- s3://model-store/retrieval/v2.3.1/
decision_space JSONB NOT NULL, -- {"actions": ["expand_query","narrow_index",...]}
eval_metric VARCHAR(64) NOT NULL, -- "recall_at_50"
eval_score FLOAT NOT NULL, -- 0.912
trained_at TIMESTAMPTZ NOT NULL DEFAULT now(),
is_active BOOLEAN NOT NULL DEFAULT FALSE,
parent_version VARCHAR(32), -- lineage: which policy this evolved from
PRIMARY KEY (agent_id, policy_version)
);
-- RL exploration buffer — stores (state, action, reward) tuples for offline batch training
CREATE TABLE rl_experience_buffer (
experience_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
agent_id VARCHAR(64) NOT NULL,
policy_version VARCHAR(32) NOT NULL,
state_snapshot JSONB NOT NULL, -- compressed user context + agent state
action_taken VARCHAR(128) NOT NULL, -- discrete action label from decision_space
local_reward FLOAT NOT NULL,
global_reward FLOAT NOT NULL,
alpha_at_time FLOAT NOT NULL, -- alpha value used for this experience
recorded_at TIMESTAMPTZ NOT NULL DEFAULT now(),
FOREIGN KEY (agent_id, policy_version) REFERENCES agent_policies(agent_id, policy_version)
);
-- Index for efficient batch retrieval during offline RL training runs
CREATE INDEX idx_experience_agent_time
ON rl_experience_buffer (agent_id, recorded_at DESC);
The parent_version field in agent_policies creates an auditable evolution lineage. MLOps teams can reconstruct the full policy trajectory of any agent — critical for debugging reward hacking incidents where the exploiting policy must be traced back to the specific training batch that introduced the drift.
Enterprise Scalability and Reliability Constraints
AgenticRS adoption typically incurs a 30% increase in development overhead initially, offset by a 40% reduction in manual model tuning time after six months of stable operation. The ROI inflection point is the month at which the cost of autonomous agent management falls below the cost of human-in-the-loop tuning.
| Dimension | Static DAG | AgenticRS | Trade-off |
|---|---|---|---|
| Initial Dev Overhead | Low | High (+30%) | Agent scaffolding, message bus, reward design |
| Manual Tuning Burden | High (ongoing) | Low (-40% after 6mo) | Agents self-adjust within policy bounds |
| Failure Blast Radius | Contained (module-scoped) | Potentially systemic | Requires collusion monitoring and circuit breakers |
| Deployment Granularity | Full pipeline redeploy | Per-agent policy update | Faster iteration, higher coordination cost |
| Observability Complexity | Moderate | High | Distributed reward tracing required |
| Adaptation to Distribution Shift | Manual retrain cycle | Autonomous (within alpha bounds) | Shift detection + alpha schedule critical |
| Time to First Inference | Low latency (simple DAG) | Higher (orchestration overhead) | Message bus latency must be budgeted |
The systemic constraint that determines production readiness is monitoring for runaway collusion between agents. This is not optional — it is a hard prerequisite for enterprise AI deployment. Systems without collusion detection will eventually develop exploiting agent coalitions that satisfy local reward criteria while degrading global user utility in ways that are invisible to standard A/B testing.
Future Trajectories in Agentic Recommendations
The AgenticRS framework as defined today represents a first-order solution to the static pipeline problem. Three research areas define the next layer of complexity that engineering teams will need to solve to mature agent deployments beyond the current state:
-
Cross-Session Agent Memory and Persistent User State Modeling Current AgenticRS implementations treat each session as an independent context window. Agents that can maintain and retrieve compressed user state across sessions — without violating privacy constraints — will close the gap between per-request optimization and long-horizon user utility. The technical challenge is defining what constitutes a privacy-preserving, evolvable memory representation that agents can update without retraining.
-
Compositional Evolution Beyond Pairwise Agent Selection The AgenticRS paper distinguishes individual agent evolution from compositional evolution over how multiple agents are selected and connected. Current implementations handle individual evolution well; compositional evolution — where the orchestrator itself learns optimal agent topologies — remains largely unsolved at production scale. The search space for agent graph structures is combinatorially large and requires constrained architecture search methods analogous to NAS for standard neural networks.
-
Causal Reward Decomposition in Overlapping Agent Influence Zones When retrieval, ranking, and filtering agents all influence the same final recommendation slate, attributing outcome variance to individual agents is a causal inference problem, not a correlation problem. Teams will need causal graph models over the agent influence structure to correctly assign reward credit — and to detect when an agent is free-riding on another agent's policy improvement rather than contributing genuinely.
Summary of Engineering Implications
The migration to AgenticRS is a tractable engineering project, not a research prototype. The functional closure criteria provide a deterministic gate for module promotion. The layered reward formula provides a mathematical constraint for alignment. The orchestration layer, built on LangGraph or an equivalent actor-model system, provides the runtime substrate.
Architect's Decision Matrix: Static Pipeline vs. AgenticRS
| Decision Factor | Choose Static Pipeline | Choose AgenticRS |
|---|---|---|
| Traffic volume | < 10k RPS, stable distribution | > 50k RPS, volatile user behavior |
| Retraining latency tolerance | Days acceptable | Hours or less required |
| Team ML maturity | Limited RL/agent experience | Strong MLOps + RL capability |
| Catalog/context volatility | Low (stable product catalog) | High (frequent catalog + user shift) |
| Monitoring investment available | Basic metric dashboards | Full distributed reward tracing |
| Initial time-to-value pressure | High (ship fast) | Lower (3–6 month runway acceptable) |
| Long-term tuning cost tolerance | Can absorb ongoing manual work | Must reduce human-in-the-loop burden |
The functional closure test is the architectural keystone. Every module that fails it stays a function. Every module that passes it earns its agent interface and all the orchestration complexity that comes with it. The decision to promote should be as deliberate as a schema migration — it is equally difficult to reverse once production traffic depends on it.
Keywords: Functionally Closed Loops, Agentic Recommender Systems, LangGraph, Reward Misalignment, Multi-Agent Orchestration, Inner and Outer Reward Design, DLRM, Actor-Model Systems, Offline RL, Message Bus Architecture