Originally published at twarx.com - read the full interactive version there.
Last Updated: July 17, 2026
Most AI technology deployments in financial services are solving the wrong problem entirely. The debate over whether to deploy a custom small language model (SLM) or an off-the-shelf large language model (LLM) misses the actual failure point — the handoffs between models, systems, and humans that no one bothered to architect. When financial institutions blame their stalled AI technology projects on model choice, they are almost always looking at the wrong layer of the stack.
This matters right now because Goldman Sachs is scaling production AI banking agents while most institutions are still stuck in pilot purgatory. The tooling — LangGraph, AutoGen, MCP, fine-tuned SLMs, RAG over vector databases — is production-ready. The coordination is not.
By the end of this piece you'll know exactly when an SLM beats an LLM, how to architect the layer between them, and how to avoid the failure mode killing 80% of enterprise AI projects. If you want the practical framing first, our overview of AI agents in production pairs well with everything below.
The typical financial-services AI stack: multiple models, RAG pipelines, and human review — all connected by a coordination layer most teams never design. This is where the AI Coordination Gap lives.
Overview: Why the SLM vs LLM Debate Is a Trap
When Goldman Sachs advanced its AI banking agents into wider deployment in 2026, the headline read like a model-selection story. It wasn't. Internally, the hard part was never choosing GPT-class reasoning versus a compact fine-tuned model. The hard part was making twelve systems — core banking, compliance rules engines, CRM, document stores, risk models — hand off cleanly to each other without hallucinated data leaking into a regulated decision.
A parallel 2026 signal — that U.S. and Japanese companies struggle with different parts of AI adoption — reveals the same truth from another angle. U.S. firms over-index on capability: bigger models, more GPUs. Japanese firms over-index on process integration and consensus. Both stall for the same underlying reason. The connective tissue between AI and existing operations is under-engineered. The McKinsey state-of-AI research repeatedly finds the same bottleneck: value comes from workflow redesign, not raw model horsepower. Similar conclusions appear in the Stanford HAI AI Index, which tracks how deployment maturity lags model capability across industries.
Here's the counterintuitive claim that should reframe your entire 2026 roadmap: the financial institutions winning with AI technology aren't the ones with the most capable models — they're the ones who solved coordination. A six-step compliance pipeline where each step is 97% reliable is only 83% reliable end-to-end. In regulated finance, that 17% gap is a regulatory incident waiting to be filed.
This article introduces a framework — The AI Coordination Gap — to name that missing layer, then breaks it into deployable components. We'll cover: when a custom SLM genuinely outperforms an off-the-shelf LLM (it's more often than vendors admit), how the two coexist in a routing architecture, real deployments at Goldman Sachs, JPMorgan, and Bloomberg, the exact cost tradeoffs, and the mistakes that quietly destroy ROI.
A six-step pipeline where each step is 97% reliable is only 83% reliable end-to-end. In regulated finance, that gap isn't a bug — it's a compliance filing.
The short version of our thesis: deploy small, specialized SLMs for the 80% of high-volume, narrow, latency-sensitive, privacy-critical tasks; reserve off-the-shelf LLMs for the 20% requiring broad reasoning; and invest the majority of your engineering effort in the orchestration layer between them. Model choice is a 20% decision. Coordination is the 80%.
Coined Framework
The AI Coordination Gap
The AI Coordination Gap is the unmanaged space between individually reliable AI components — models, retrieval systems, tools, and human reviewers — where errors compound, context is lost, and accountability disappears. It names why financial AI pilots pass in isolation but fail in production.
What Is a Custom SLM — and Why Finance Cares
A small language model (SLM) is a language model — typically 1B to 15B parameters — that's small enough to fine-tune, host, and run cheaply, often on-premise or in a private VPC. Think Microsoft Phi-3, Mistral 7B, Llama 3.1 8B, or Google Gemma 2. An off-the-shelf LLM is a frontier general-purpose model accessed via API — OpenAI's GPT-4-class models, Anthropic's Claude, or Google's Gemini.
For financial services, three properties make SLMs disproportionately attractive.
Data residency and privacy. A fine-tuned SLM can run entirely inside your compliance perimeter. No customer PII, no material non-public information, no trade data ever leaves your infrastructure. For a bank governed by GLBA, SOX, or MiFID II, this alone often decides the architecture before any accuracy benchmark gets run.
Latency and cost at volume. A fraud-scoring model that sees 4 million transactions a day cannot economically call a frontier LLM API for each one. Full stop. A quantized 7B SLM running on your own GPUs returns a decision in 40–90ms at a fraction of the marginal cost.
Narrow-task accuracy. On a well-scoped task — classifying a loan document, extracting fields from a KYC form, flagging a suspicious transaction narrative — a fine-tuned SLM frequently beats a general LLM because it's been trained on your exact taxonomy and your edge cases, not the internet's. NVIDIA's own research team has argued in NVIDIA Research that small language models are the future of agentic AI precisely for these economics.
40x
Lower inference cost of a fine-tuned 7B SLM vs a frontier LLM API at high volume
[arXiv, 2025](https://arxiv.org/)
83%
End-to-end reliability of a 6-step pipeline where each step is 97% accurate
[arXiv, 2025](https://arxiv.org/)
~80%
Enterprise generative AI projects that fail to reach production value
[Gartner, 2025](https://www.gartner.com/en)
Andrew Ng, founder of DeepLearning.AI, has repeatedly argued that the largest near-term enterprise value comes not from bigger models but from agentic workflows that decompose tasks. That decomposition is exactly where SLMs shine — and exactly where coordination breaks. His teaching at DeepLearning.AI is a useful grounding for teams new to agentic design. For a broader survey of the economics, the a16z AI research library tracks the same shift toward smaller, specialized models in production.
For narrow, repeated financial tasks, a fine-tuned Mistral 7B often matches GPT-4-class accuracy at roughly 1/40th the marginal inference cost. The frontier model's advantage disappears the moment the task stops being open-ended.
SLMs win on cost, latency, and data residency for narrow tasks; LLMs win on open-ended reasoning. Most banks need both, connected by multi-agent orchestration.
The AI Coordination Gap: A 5-Layer Framework
The reason the SLM-vs-LLM question is a trap is that it treats models as the system. They're components. The system is the coordination layer around them. Here are the five layers where financial AI technology actually succeeds or fails.
Coined Framework
The AI Coordination Gap
It's the architectural blind spot where teams optimize each model in isolation while leaving the routing, context-passing, verification, and escalation between them undesigned. Close the gap and 83% reliability becomes 99%; ignore it and no model upgrade will save you.
Layer 1 — The Routing Layer
The router decides, per request, whether a task goes to a cheap SLM, a frontier LLM, a deterministic rules engine, or a human. This is a classification problem, and it's best solved by a small model or even a heuristic — not a frontier LLM. A loan-eligibility check with clean inputs routes to the SLM; an ambiguous customer complaint with legal exposure routes to Claude with human review.
In practice, teams build this with LangGraph's conditional edges or a lightweight classifier. The router is the single highest-leverage component in the stack because it controls cost, latency, and risk simultaneously. I'd prioritize building it before anything else.
Layer 2 — The Retrieval Layer (RAG)
Retrieval-Augmented Generation grounds every model — SLM or LLM — in your actual documents: policy manuals, product terms, regulatory filings, customer histories. It runs over a vector database like Pinecone, Weaviate, or pgvector. In finance, RAG isn't optional: a model must cite the exact clause of the exact policy version, and that citation must be auditable. Non-negotiable.
Layer 3 — The Tool & Context Layer (MCP)
Models are useless without access to live systems — the core banking ledger, the risk model, the CRM. The Model Context Protocol (MCP), introduced by Anthropic in late 2024 and now widely adopted, standardizes how models call these tools. Before MCP, every integration was bespoke. MCP turns them into reusable, permissioned connectors — which matters enormously when a regulator asks exactly which systems an agent touched during a specific transaction.
Layer 4 — The Verification Layer
This is the layer almost everyone skips. It's also where the Coordination Gap is deadliest. Every model output crossing into a regulated decision must be verified — against RAG citations, against deterministic rules, against confidence thresholds. A generated payment instruction gets checked against the ledger before execution. Verification is what converts 83% pipeline reliability into 99%+. Skip it and you're just hoping.
Layer 5 — The Escalation & Audit Layer
When confidence is low or exposure is high, the system escalates to a human with full context — not a bare flag with no explanation. Every decision, every model version, every retrieved document, and every human override gets logged immutably. In finance this isn't optional polish; it's the difference between an explainable decision and an unexplainable liability when the examiner calls. The NIST AI Risk Management Framework makes this traceability an explicit expectation, and the EU AI Act codifies it for high-risk financial systems.
Financial AI Coordination Architecture: SLM + LLM Routing With Verification
1
**Router (SLM classifier / LangGraph edge)**
Input: incoming request + metadata. Classifies task complexity and risk. Outputs a route decision in <30ms. Low-risk narrow task → SLM; open-ended/high-exposure → LLM; deterministic → rules engine.
↓
2
**Retrieval (RAG over Pinecone/pgvector)**
Fetches the exact policy version, customer record, or filing. Returns cited chunks with document IDs for auditability. Latency budget: 50–150ms.
↓
3
**Generation (fine-tuned SLM or frontier LLM)**
Produces grounded output. SLM path: 40–90ms, on-prem, private. LLM path: 800–2000ms via API for complex reasoning. Both must cite retrieved sources.
↓
4
**Tool Execution (MCP connectors)**
Calls ledger, risk model, or CRM through permissioned MCP servers. Every call is scoped, logged, and reversible where possible.
↓
5
**Verification (rules + citation check + confidence)**
Cross-checks output against deterministic rules and RAG citations. If confidence < threshold or rule conflict → escalate.
↓
6
**Escalation & Audit Log**
Human review with full context on escalation. Immutable log of model version, sources, tools, and overrides for regulatory replay.
This sequence matters because reliability is multiplicative — the verification and audit layers are what turn a fragile 83% pipeline into a defensible 99%+ system.
Model choice is a 20% decision. Coordination is the other 80%. Yet 90% of enterprise AI budgets are spent arguing about the 20%.
How Each Layer Works in Practice
Let's make this concrete with the highest-value financial use case: automated customer complaint handling with compliance guardrails — a task Goldman Sachs, JPMorgan, and every retail bank cares about deeply.
A customer message arrives. The router — a fine-tuned DistilBERT-class classifier, effectively an SLM — reads it in under 30ms and determines: is this a simple balance query (→ SLM), a dispute with potential legal exposure (→ LLM + human), or a fraud signal (→ rules engine + escalation)? Ninety percent of volume is simple and never touches a frontier model at all.
For the simple path, RAG pulls the customer's account state and the relevant product terms from a vector index. The fine-tuned SLM — say a Llama 3.1 8B tuned on the bank's own resolved tickets — drafts a grounded response. The verification layer confirms every factual claim maps to a retrieved source and that no prohibited language appears: guarantees, unauthorized advice, anything that becomes a liability. Passes? It sends. Fails? Escalates.
You can prototype this coordination logic quickly. Here's a minimal LangGraph router pattern:
python — LangGraph conditional routing
Route between a cheap SLM and a frontier LLM based on risk
from langgraph.graph import StateGraph, END
def router(state):
# SLM classifier returns risk + complexity scores
risk = classify_risk(state['message']) # 0-1, fine-tuned SLM
if risk > 0.7:
return 'llm_with_review' # high exposure
if is_deterministic(state['message']):
return 'rules_engine' # e.g. balance lookup
return 'slm_fast_path' # 90% of volume
graph = StateGraph(dict)
graph.add_node('slm_fast_path', run_slm) # fine-tuned Llama 8B, on-prem
graph.add_node('llm_with_review', run_llm_review) # Claude + human queue
graph.add_node('rules_engine', run_rules)
graph.add_node('verify', verify_output) # citation + rule check
graph.set_conditional_entry_point(router)
graph.add_edge('slm_fast_path', 'verify')
graph.add_edge('llm_with_review', 'verify')
graph.add_edge('verify', END) # escalates internally on fail
app = graph.compile()
This is deliberately simple, but it encodes the entire thesis: the router and verifier — the coordination — are the system. Swapping the underlying model is a one-line change. If you want ready-made routing and verification patterns, explore our AI agent library for finance-specific templates.
A production routing graph built in LangGraph: the coordination logic — not the model — determines cost, latency, and compliance outcomes.
For teams that prefer a lower-code orchestration surface, n8n can wire the same routing, RAG calls, and human-in-the-loop escalation visually — useful when your operations team, not just engineers, needs to maintain the flow. See our guide to workflow automation with n8n for finance patterns, and our deeper RAG and vector database guide for grounding the retrieval layer.
The router should almost never be a frontier LLM. Using GPT-4-class reasoning to decide where to send a request adds 800ms+ of latency and 10x cost to a decision a 300M-parameter classifier makes in 20ms. This single mistake inflates most banks' AI bills by 3–5x. I've seen it happen repeatedly.
Real Deployments: Goldman Sachs, JPMorgan, Bloomberg
Goldman Sachs has deployed an internal AI assistant to thousands of employees and, in 2026, advanced toward agentic banking workflows. The public framing is about the assistant's capability; the operational reality is a governance and coordination architecture that controls which systems an agent may touch and mandates human sign-off on material actions. Marco Argenti, Goldman's CIO, has publicly described their approach as treating AI agents like new employees who must be onboarded, permissioned, and supervised — a coordination framing, not a model-capability one. That framing matters. You can read more from Goldman Sachs Insights directly.
JPMorgan Chase runs COiN and a suite of internal LLM tools, and its LOXM trading systems predate the current wave entirely. Jamie Dimon has repeatedly emphasized that the value is in workflow integration across the firm's data — again, coordination over raw capability. JPMorgan reportedly reviewed 12,000 commercial-credit agreements in seconds with document AI, work that consumed 360,000 lawyer-hours annually, as covered by Bloomberg Technology. That number comes from the pipeline, not from one model being smarter than another.
Bloomberg built BloombergGPT, a 50B-parameter model trained on financial data — effectively a large domain SLM. The instructive lesson, documented in the original BloombergGPT paper on arXiv: within a year, general frontier models with good RAG began matching it on many tasks. That validates something worth internalizing. For most institutions, a smaller fine-tuned model plus strong retrieval and coordination beats training a giant bespoke model from scratch.
DimensionCustom SLM (fine-tuned 7-15B)Off-the-Shelf LLM (frontier API)
Marginal cost at high volumeVery low (own GPUs)High (per-token API)
Latency40-90ms800-2000ms
Data residencyFull on-prem/VPC controlData leaves perimeter (mitigable)
Narrow-task accuracyHigh (tuned on your data)High but generic
Open-ended reasoningLimitedExcellent
Upfront effortHigh (data + tuning + hosting)Low (API key)
Best forHigh-volume narrow regulated tasksComplex, low-volume reasoning
Bloomberg spent millions training a 50B financial model. Within a year, a smaller model plus good retrieval matched it. The moat was never the model — it was the data and the coordination around it.
[
▶
Watch on YouTube
How Goldman Sachs and JPMorgan Are Deploying Production AI Agents
Enterprise AI in financial services
](https://www.youtube.com/results?search_query=goldman+sachs+ai+agents+enterprise+deployment)
What It Costs and What You Need to Implement This
A realistic first production deployment of a coordinated SLM+LLM system in a mid-sized bank looks like this over 6 months.
Data & fine-tuning: You need 2,000–20,000 labeled examples per narrow task. Fine-tuning a 7B model on your infrastructure costs comparatively little in compute — the expensive part is data curation and evaluation, not GPUs. Andrej Karpathy's public teaching on fine-tuning economics makes this point clearly: a few thousand high-quality examples almost always beat tens of thousands of noisy ones. Don't let anyone sell you on data volume over data quality. The open-source ecosystem around Hugging Face makes this workflow accessible to most teams.
Infrastructure: On-prem or VPC GPUs to host the SLM (a couple of A100/H100-class GPUs serve substantial volume), a vector database (Pinecone managed or pgvector self-hosted), and an orchestration runtime (LangGraph, which is production-ready, or AutoGen and CrewAI for multi-agent patterns — both are maturing but still moving fast, so pin your versions).
The coordination layer is where most of the engineering time actually goes — routing logic, verification rules, MCP tool connectors, audit logging. Budget 60% of engineering hours here. This is the part vendors never quote you, and it's the part that determines whether the project ships value or quietly dies six months post-launch. Our breakdown of AI orchestration patterns goes deeper on how to structure that spend.
❌
Mistake: Using a frontier LLM for everything
Teams pipe every request through GPT-4-class APIs 'to be safe.' At 4M transactions/day this is economically impossible and adds seconds of latency to tasks that need milliseconds.
✅
Fix: Build a router first. Send 80-90% of narrow volume to a fine-tuned SLM (Llama 3.1 8B or Mistral 7B) and reserve the LLM for high-exposure reasoning only.
❌
Mistake: Skipping the verification layer
Pilots pass because a human eyeballs every output. In production that human disappears, and a 3% per-step error rate compounds across the pipeline into regulatory incidents.
✅
Fix: Add deterministic rule checks and RAG citation verification between generation and execution. Escalate anything below a confidence threshold to a human with full context.
❌
Mistake: Fine-tuning when RAG would do
Banks fine-tune models on facts that change weekly (rates, policies), then re-tune constantly. Fine-tuning teaches behavior; it's the wrong tool for volatile knowledge.
✅
Fix: Fine-tune for task behavior and format; use RAG over a vector database for facts and policies. Update the index, not the weights.
❌
Mistake: No audit trail for agent actions
When an agent touches the ledger or CRM without logging model version, sources, and tool calls, you cannot answer a regulator's 'why did the system do this?' — an automatic finding.
✅
Fix: Use MCP permissioned connectors and log every call immutably. Make every decision replayable end-to-end.
Coined Framework
The AI Coordination Gap
Every mistake above is a symptom of the same disease: teams engineer components and neglect the connective layer. The Gap is closed not by a better model but by routing, verification, and audit discipline.
A phased rollout: start with one narrow high-volume task, build the coordination layer once, then reuse it across use cases. This is how AI agents scale in regulated environments.
What Comes Next: 2026-2027 Predictions
2026 H2
**MCP becomes the default integration standard in banking**
With Anthropic's Model Context Protocol adopted by OpenAI, Google, and major tooling in 2025-2026, banks will standardize agent-to-system connections on MCP, replacing bespoke integrations and making audits far cheaper.
2027 H1
**Fine-tuned SLMs handle the majority of regulated inference volume**
As models like Phi and Mistral improve and quantization matures, cost and latency economics push 80%+ of narrow financial inference to on-prem SLMs, per the direction of arXiv efficiency research.
2027 H2
**The verification layer becomes a regulated requirement**
Following the EU AI Act's phased enforcement and U.S. sectoral guidance, auditable verification and escalation layers will shift from best-practice to mandated for high-risk financial AI decisions.
2028
**Coordination platforms displace model choice as the buying decision**
Procurement conversations shift from 'which model' to 'which orchestration and governance layer,' mirroring how cloud buying shifted from servers to platforms.
Frequently Asked Questions
What is agentic AI?
Agentic AI refers to systems where language models don't just answer questions but take actions — calling tools, querying databases, executing multi-step plans, and making decisions toward a goal. In finance, an agent might read a customer complaint, retrieve the account record via RAG, check policy, draft a resolution, and route it for approval. Frameworks like LangGraph, AutoGen, and CrewAI provide the scaffolding. The distinction that matters is autonomy plus tool use: an agent perceives, decides, and acts in a loop rather than producing a single response. In regulated environments, agentic systems require verification and audit layers because they can trigger real-world effects — moving money, updating records — where an error has direct consequences. Production agentic deployments almost always keep a human in the loop for high-exposure actions.
How does multi-agent orchestration work?
Multi-agent orchestration coordinates several specialized agents — each handling a sub-task — toward a shared goal. A supervisor or graph routes work: one agent retrieves documents, another drafts, another verifies compliance, another executes tool calls. LangGraph models this as a stateful graph with nodes and conditional edges; AutoGen uses conversational agents that message each other; CrewAI assigns role-based agents. The critical design choice is the coordination protocol — how context passes between agents and how failures escalate. In finance, orchestration must be deterministic where regulation demands it: a rules engine or verification node overrides probabilistic agents. The most common failure is compounding error, where each agent is 95% reliable but the chain drops well below acceptable thresholds. Solving that requires explicit verification steps and immutable audit logging between every handoff.
What companies are using AI agents?
In financial services, Goldman Sachs deployed an internal AI assistant to thousands of staff and is advancing agentic banking workflows in 2026. JPMorgan Chase runs COiN for contract analysis — reportedly saving hundreds of thousands of lawyer-hours — plus internal LLM tooling. Morgan Stanley built an advisor assistant on OpenAI models grounded in its research library. Bloomberg built BloombergGPT for financial NLP. Beyond finance, Klarna, Salesforce, and Intuit have shipped customer-facing agents. The pattern across all of them: the winners invested heavily in coordination, governance, and retrieval — not just model access. Named leaders like Goldman's CIO Marco Argenti frame agents as onboarded, permissioned digital employees, which reflects how seriously mature deployments treat access control and supervision rather than treating agents as unsupervised automation.
What is the difference between RAG and fine-tuning?
Fine-tuning updates a model's weights to change its behavior, format, or domain style — it teaches the model how to respond. RAG (Retrieval-Augmented Generation) injects relevant documents into the prompt at query time from a vector database — it gives the model what to know. The rule of thumb: fine-tune for behavior and tone; use RAG for facts, especially volatile ones like rates, policies, or customer records. In banking, fine-tuning an SLM on your resolved-ticket style plus RAG over your current policy documents is the ideal combination. Fine-tuning facts that change weekly is a costly mistake — you'd re-tune constantly. RAG updates instantly by re-indexing. Many teams also combine both with tool calls via MCP. Fine-tuning requires labeled data and compute; RAG requires an embedding pipeline and a vector store like Pinecone or pgvector.
How do I get started with LangGraph?
Start by installing LangGraph (pip install langgraph) and modeling your workflow as a state graph: define a shared state object, add nodes for each step (retrieval, generation, verification), and connect them with edges. Use conditional edges for routing — for example, sending low-risk requests to an SLM and high-risk ones to an LLM with human review. Begin with a single narrow task rather than a sprawling agent. Add a verification node before any action that touches production systems. LangGraph is production-ready and integrates with LangChain's retrieval and tool ecosystem. Read the official docs at python.langchain.com, then build a minimal router-plus-verifier graph before adding complexity. The biggest early win is keeping coordination logic explicit and testable. For finance-specific starter templates covering routing and audit logging, adapt patterns from established agent libraries rather than building from scratch.
What are the biggest AI failures to learn from?
The most instructive failures share one root cause: neglecting the coordination layer. Air Canada's chatbot invented a refund policy the airline was legally forced to honor — no verification layer caught the hallucination. Zillow's home-buying algorithm mispriced at scale and contributed to shutting down a business unit — no adequate guardrails between prediction and action. Numerous banking pilots pass in isolation then fail in production because compounding per-step errors were never measured end-to-end. The lesson for financial services is consistent: individually accurate models produce inaccurate systems without verification, escalation, and audit trails. Frontier model capability rarely causes these failures; the undesigned handoffs between components do. Before scaling any deployment, stress-test the full pipeline's reliability, not each component's, and instrument every decision so it can be explained to a regulator.
What is MCP in AI?
MCP, the Model Context Protocol, is an open standard introduced by Anthropic in late 2024 for connecting AI models to external tools, data sources, and systems in a consistent, permissioned way. Before MCP, every integration between a model and a system — a database, CRM, or ledger — was custom-built and hard to audit. MCP standardizes these connections as reusable 'servers' that expose tools and resources with defined permissions. For financial services this is significant: it makes agent access to core systems explicit, scoped, and loggable, which regulators require. MCP has since gained broad adoption across the ecosystem, including support from OpenAI and major tooling. In a coordinated architecture, MCP sits at the tool-and-context layer, letting your router and agents call live systems safely. It's production-usable today and increasingly the default way to give agents controlled, auditable access to enterprise infrastructure.
The bottom line for financial services in 2026: stop debating which model to buy and start engineering the layer between your models. Deploy custom SLMs for the high-volume, narrow, privacy-critical majority; reserve off-the-shelf LLMs for genuine open-ended reasoning; and pour your engineering effort into routing, retrieval, verification, and audit. Whichever AI technology stack you standardize on, close the AI Coordination Gap and your 83% pipeline becomes a 99% production system. Ignore it, and no model upgrade will ever save you. Ready to build? Start with our finance-ready AI agent templates and the deeper walkthrough in our multi-agent systems guide.
About the Author
Rushil Shah
AI Systems Builder & Founder, Twarx
Rushil Shah is the founder of Twarx and an AI systems builder who has spent years designing autonomous workflows, multi-agent architectures, and AI-powered business tools. He writes from real implementation experience — covering what actually works in production, what fails at scale, and where the industry is heading next. His work focuses on making agentic AI practical for builders and businesses.
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