The Enterprise-Class Agent Engineering Stack : From Pilot to Production-Grade Agentic Systems

Graph-RAG knowledge fabric + AgentEvolver runtime, wired into your existing operational platforms.

Technical Abstract

The first large-scale study of production AI agents (Berkeley, December 2025; 306 practitioners, 20 case studies) reveals a striking gap: 68% of deployed agents execute at most 10 steps before requiring human intervention, 74% depend on human evaluation, and no team applies standard reliability metrics like five 9s. Production agents are far simpler than academic literature suggests—not because teams lack ambition, but because the infrastructure to run complex agents reliably does not exist.

Agent engineering (LangChain, December 2025) addresses this by defining the practice: how teams iterate on non-deterministic systems through build → test → ship → observe → refine cycles. It treats non-determinism as the core engineering challenge and human iteration as the solution. AgentEvolver (Alibaba, November 2025) goes further, demonstrating mechanisms—self-questioning, self-navigating, self-attributing—that enable models to improve autonomously during training, reducing dependency on curated datasets.

Neither specifies a production architecture where improvement is governed, continuous, and verifiable at runtime. The practice requires humans in the loop. The mechanisms operate at training time. Enterprise agents need both—plus governed context, binding evaluation, and causal artifact flow—at runtime.

This report defines the Enterprise-Class Agent Engineering Stack—a closed-loop production architecture combining:

1. Governed context substrate (UCL): All agents consume one semantic truth—Context Packs with contracts, lineage, and KPI alignment—reused across applications, not per-agent RAG with divergent definitions.

2. Runtime self-improvement: AgentEvolver mechanisms operating on production traces and verified outcomes. The base model stays frozen; operational artifacts evolve.

3. Evaluation as control plane: Binding gates—quality, safety, cost, latency—that block execution in real-time. No eval pass, no execute.

4. Causal artifact connectivity: Each pillar produces artifacts consumed by the next. Improvement compounds; governance has no gaps.

The stack runs on your existing investments—LangChain, LangGraph, Neo4j, Snowflake, Temporal, LangSmith—no rip-and-replace. No existing public framework specifies this integrated architecture.

[FIGURE 1: The Agent Engineering Stack]

1. The Architectural Gap

1.1 What the Data Shows

The Berkeley study's findings quantify what practitioners experience daily:

Finding	Implication
68% of agents execute ≤10 steps before human intervention	Complex autonomous workflows don't survive production
74% depend on human evaluation	Automated quality gates don't exist or don't work
70% use prompting, not fine-tuning	Teams optimize for iteration speed, not model performance
37.9% cite reliability as #1 challenge	Governance and compliance are secondary to "does it work"
No team applies five 9s metrics	Agent reliability lacks even basic measurement frameworks

These aren't failures of engineering talent. They're symptoms of missing infrastructure.

[FIGURE 2: What We Learned — 11 Findings from 306 Practitioners]

1.2 Agent Engineering Is Necessary But Not Sufficient

Agent engineering captures a genuine shift. The discipline recognizes that agents fail in ways that look like systems failures: a system can be up, fast, and still wrong in ways that matter to the business.

The practice is iterative. But the practice describes how to iterate. It does not specify:

• What the agent reasons over: Where does context come from? How is semantic consistency enforced across agents?

• How improvement happens: What changes when the system learns? How are changes validated before promotion?

• How actions are verified: Did the agent's action achieve the intended outcome in the system of record?

• How evaluation gates execution: When does an action proceed? What blocks unsafe or incorrect outputs?

1.3 Why Enterprise Deployments Fail

Enterprise deployments introduce failure drivers that agent frameworks alone do not address:

Context fragmentation. Enterprise truth is scattered across BI layers, feature stores, RAG pipelines, and agent tools. Each consumption model reshapes joins, KPIs, and dimensions differently. Agents operating on fragmented context behave unpredictably even with strong LLMs.

Unverified tool actions. Once agents write to systems of record (ITSM, CRM, ERP), the system must control blast radius. Actions must be authorized, scoped, traceable, and reversible.

Evaluation disconnected from execution. Most agent systems treat evaluation as observability—dashboards, periodic audits, offline test suites. The 74% human evaluation dependency exists because binding automated gates don't exist. Post-hoc reports document incidents; they don't prevent them.

No causal artifact chain. Without explicit artifact flow between stages, each component operates in isolation. The system cannot improve itself.

1.4 The Missing Layer

The gap is precise:

• LangChain-style agent engineering defines the practice—human-driven iteration

• AgentEvolver-style self-improvement defines the mechanisms—but applies them during training

• Neither specifies a production architecture that makes enterprise behavior stable, auditable, and continuously improvable at runtime

[FIGURE 3: Enterprise-Class Agent Engineering — The Path from Pilot to Production-Grade Agentic Systems]

2. The Six-Pillar Architecture

The stack is built on six pillars forming a closed loop. Each pillar produces artifacts consumed by the next.

2.1 The Production Loop

Stage	What Happens
Signals	Production telemetry, business KPIs, drift alerts trigger the loop
Context	Governed context assembled into evaluated Context Packs
Evals	LLM judges + KPI gates produce binding verdicts
Execute + Verify	Binding gate enforced; scoped tool actions with rollback-ready controls; outcome verification against intent; evidence captured
Evolution	Experience pool updates; winning patterns promoted with rollback semantics

The loop is fail-closed: no eval pass, no execute. Every stage produces artifacts enabling audit, replay, and rollback.

2.2 The Six Pillars

Pillar	What It Does
AgentEvolver Runtime	Governs the self-improving loop; routes work across models, agents, and playbooks; learns from every deployment
UCL + Knowledge Graph & Graph-RAG	Unified context with domain knowledge graph; feeds agents with explainable, multi-hop context reused across applications
Signals, Telemetry & Drift Fabric	Unified signal bus for logs, tickets, KPIs, incidents; drives anomaly detection and trigger conditions
Eval, Guardrails & Evidence Harness	Quality, safety, cost, and latency evals; produces scorecards and audit-ready evidence packs
Intelligent Automations & Agent Packs	Pre-built agents and runbooks that move KPIs—not just answer questions
Operational Platforms	Runs on your LLM/MLOps platforms and workflow engines; plugs into ITSM/CRM/ERP—no new system of record

[FIGURE 4: Technology Highlights — What Makes the Stack Work]

3. Technology Deep Dive

3.1 AgentEvolver Runtime (Self-Improving Loop)

The orchestration core implementing continuous improvement of operational artifacts based on production signals and verified outcomes.

Core Capabilities:

• Governed loop: signals → context → evals → execute + verify → evolution

• Routes work across models, agents, and playbooks based on intent type and historical performance

• Experience pool stores (context, action, outcome, verdict) tuples; retrieves similar executions to bias toward successful paths

• Versioned promotion with automatic rollback on regression

Runtime Self-Improvement Mechanisms:

Alibaba's AgentEvolver demonstrates that smaller models outperform larger ones with proper self-improvement scaffolding. This stack applies the same mechanisms at production runtime:

Mechanism	Training-Time (AgentEvolver)	Runtime (This Stack)
Self-Questioning	Synthesize training tasks	Generate routing rule variants; propose tool constraints
Self-Navigating	Experience-guided exploration	Retrieve similar executions; bias toward successful paths
Self-Attributing	Credit assignment for backprop	Trace failures to context rules; identify failing modules

The base model is frozen. Operational artifacts—routing rules, prompt modules, tool constraints, context composition policies—evolve based on verified outcomes.

Integrates with: LangGraph, Temporal, n8n, workflow orchestration platforms

Artifacts: ExecutionTraceID, ChangeManifestID, PromotionRecordID

3.2 UCL + Knowledge Graph & Graph-RAG

UCL (Unified Context Layer) provides the governed context substrate that all agents consume.

Core Capabilities:

• Contracts-as-Code: KPI definitions, entity schemas, join paths, freshness requirements enforced at runtime

• Context Packs: Pre-assembled, evaluated bundles with retrieval results, semantics, and lineage—reused across applications

• Graph-RAG: Multi-hop retrieval with entity/relation consistency via domain knowledge graph; explainable provenance

• Shared substrate: BI, ML features, RAG, and agents consume the same governed context—no semantic forks

[FIGURE 5: Agentic Systems Need UCL — One Substrate for Many Copilots]

Integrates with: Neo4j, LangChain, Snowflake, Databricks, Fabric

Artifacts: ContextPackID, ContextPackManifest, ContractVersionSetID

3.3 Signals, Telemetry & Drift Fabric

Unified signal bus driving runtime behavior—not just observability dashboards.

Core Capabilities:

• Unified signal bus for logs, tickets, KPIs, incidents, and feedback

• Drift and freshness monitoring with trigger conditions feeding the runtime

• Anomaly detection driving evaluation, escalation, and evolution

• KPI movement tracking linking operational signals to business metrics

Integrates with: OpenTelemetry, Datadog, Monte Carlo, observability platforms

Artifacts: SignalEventID, DriftAlertID, TriggerConditionID

3.4 Eval, Guardrails & Evidence Harness

Evaluation as control plane with policy-as-code guardrails.

Core Capabilities:

• Quality, safety, cost, and latency evals: LLM judges assess correctness, safety, policy compliance—grounded against Context Packs

• Binding eval gates: faithfulness, answerable@k, cite@k, latency budgets that block execution when thresholds are not met

• Policy-as-code: SoD, approval workflows, rollback policies as executable constraints

• Scorecards and evidence packs: Every governed change generates KPI-linked scorecards and complete audit chains

Integrates with: LangSmith, W&B, ServiceNow, custom judge frameworks

Artifacts: EvalVerdictID, JudgeVerdictSetID, EvidenceRecordID

3.5 Intelligent Automations & Agent Packs

Pre-built, graph-aware agents and runbooks delivering business outcomes—not just answering questions.

Core Capabilities:

• Domain-specific agents for incidents, changes, support, quarter-close

• Typed intents and policies orchestrating end-to-end actions that move KPIs

• Graph-aware reasoning leveraging the knowledge graph for explainable decisions

• Runbook automation with full audit trail and rollback

Integrates with: ServiceNow, Salesforce, SAP, ITSM/CRM/ERP systems

Artifacts: AgentPackID, RunbookExecutionID, IntentResolutionID

3.6 Operational Platforms (LLM / ML / Workflow)

The stack runs on existing infrastructure—governing layer, not replacement.

Core Capabilities:

• Uses your LLM/MLOps platforms, agent frameworks, workflow engines, and automation tools

• Connectors through MCP and standard APIs to ITSM/CRM/ERP

• No new system of record: evidence, context, and signals flow through existing infrastructure

Integrates with: LangChain, LangGraph, Bedrock, Azure AI, OpenAI, Snowflake, Databricks, ServiceNow, Salesforce

Artifacts: ConnectorManifestID, ToolCatalogVersionID, PlatformIntegrationID

4. Why Partial Adoption Fails

The six pillars form a causal system. Artifacts flow between pillars; removing any one breaks the chain.

If You Remove...	The Loop Breaks Because...
AgentEvolver Runtime	No orchestration, no evolution—static system
UCL + Graph-RAG	Agents reason over fragmented, inconsistent context
Signals & Drift Fabric	No triggers, no drift detection—blind runtime
Eval & Evidence Harness	No gates, no audit trail—ungoverned execution
Agent Packs	No pre-built patterns—every workflow from scratch
Operational Platforms	No integration—stack is theoretical

Partial adoption consequences:

Missing Combination	Consequence
UCL without Signal Fabric	Context is governed but stale—no drift detection
Signal Fabric without Eval Harness	Triggers fire but nothing gates execution—alert fatigue
Eval Harness without UCL	Judging outputs against unstable inputs—metrics are noise
Agent Packs without Eval Harness	Automations run ungoverned—blast radius uncontrolled
Runtime without Verification	Optimizing for model assertions, not real outcomes

Partial adoption creates open loops where improvement cannot compound and governance has gaps.

5. Competitive Positioning

Framework	What It Provides	What It Lacks
LangChain / LangGraph	Agent orchestration, tool calling	No governed context layer, no runtime evolution
AgentEvolver (Alibaba)	Self-improvement mechanisms	Training-time only; static once deployed
MLOps platforms (MLflow, W&B)	Model lifecycle management	Model-centric, not agent-centric; no action verification
Workflow engines (Temporal, Airflow)	Durable execution	No evaluation gates, no experience pool
This Stack	Governed context + runtime evolution + binding evals + causal artifact flow	—

The enterprise gap: everyone has pieces. No one has the integrated loop.

6. Deployment Models

Full Stack (Greenfield). New agent deployments get all six pillars from day one.

Overlay (Brownfield). Governing layer on existing LangChain/LangGraph investments. Adds UCL context governance, binding evals, and evolution.

Platform-Agnostic. Runs on Snowflake, Databricks, Fabric, SAP BTP. Same contracts, semantics, and evidence model across platforms.

Pillar-by-Pillar. Start with UCL + Eval Harness (governance foundation). Add Signals + Runtime as operational maturity grows.

Conclusion

The data is clear: 68% of production agents can't exceed 10 steps without human intervention. 74% depend on human evaluation because binding automated gates don't exist. No team applies standard reliability metrics. Reliability—not governance, not compliance—is the #1 challenge.

This isn't a talent problem. It's an infrastructure problem.

Agent engineering established the practice. AgentEvolver demonstrated the mechanisms. What's missing is the production architecture that makes both work at enterprise scale—where context is governed, evaluation gates execution, actions are verified against systems of record, and improvement happens continuously without human heroics.

The Enterprise-Class Agent Engineering Stack provides that architecture:

• One context substrate replacing fragmented per-agent RAG—so agents reason over consistent, governed truth

• Self-improvement at runtime applying AgentEvolver mechanisms to production traces—so the system learns from deployment, not just training

• Binding evaluation gates that block execution in real-time—so unsafe or incorrect outputs never reach production

• Causal artifact flow connecting every stage—so improvement compounds and governance has no gaps

The shift is structural: from agents as experiments requiring constant human intervention to agents as governed infrastructure that improves itself. From deploy-and-pray to deploy-and-verify. From 74% human evaluation dependency to evaluation as an automated control plane.

This is the threshold at which agents become dependable enterprise systems.

Appendix A: Artifact Reference

Artifact	Produced By	Consumed By	Purpose
ContextPackID	UCL + Graph-RAG	Runtime, Eval Harness	Identifier for assembled context
ContextPackManifest	UCL + Graph-RAG	Runtime, Eval, Evidence	Sources, schemas, budgets
SignalEventID	Signal Fabric	Runtime, Eval Harness	Normalized production signal
DriftAlertID	Signal Fabric	UCL, Runtime	Context drift alert
EvalVerdictID	Eval Harness	Runtime	Judgment with reasoning
EvidenceRecordID	Evidence Harness	Audit, Compliance	Stitched evidence chain
ExecutionTraceID	Runtime	Runtime, Evidence	Structured execution trace
ChangeManifestID	Runtime	Runtime	Proposed changes specification
PromotionRecordID	Runtime	Evidence	Validated promotion record
AgentPackID	Agent Packs	Runtime	Pre-built agent configuration
ConnectorManifestID	Operational Platforms	Runtime	Registered connector specification
ToolCatalogVersionID	Operational Platforms	Runtime	Available tool catalog version
PlatformIntegrationID	Operational Platforms	Runtime	Platform integration configuration

Appendix B: Glossary

Agent Engineering. The iterative discipline of refining non-deterministic LLM systems. Build → test → ship → observe → refine → repeat.

AgentEvolver Runtime. The orchestration core implementing the governed self-improvement loop.

Binding Eval. An evaluation verdict that gates runtime execution. Fail-closed: no eval pass, no execute.

Context Pack. A pre-assembled bundle of governed context: retrieval results, semantics, lineage, KPI alignment. Versioned and evaluated before serving.

Contracts-as-Code. Machine-readable specifications for KPI definitions, entity schemas, join paths, and freshness requirements. Enforced at runtime.

Experience Pool. Collection of (context, action, outcome, verdict) tuples. Used for Self-Navigating retrieval and proposal generation.

Graph-RAG. Graph-aware Retrieval-Augmented Generation. Multi-hop retrieval using knowledge graph structure for entity/relation consistency.

UCL (Unified Context Layer). A governed ContextOps substrate providing contracted semantics, evaluated Context Packs, and an evidence ledger across all consumption models.

Enterprise-Class Agent Engineering Stack — Technical Architecture Report v3.10

Arindam Banerji, PhD

banerji.arindam@gmail.com