My AI Agent Takes 3 Seconds Per Decision — Can a Compiled Path Meet Your Latency Target?

TL;DR

AI-agent loops can accumulate multiple live-model calls. Some are genuinely generative; others repeatedly choose among stable outcomes such as routes, moderation states, or approval levels. Instrument the loop first, then test whether a compiled classifier can remove selected live calls while preserving per-class quality and improving the measured end-to-end latency and credit profile.

The Problem: Every Decision Is an LLM Call

Here's a typical agent loop. The agent receives a user message, decides what to do, calls a tool, evaluates the result, and decides the next step. Each "decide" step is an LLM call:

# Typical agent loop — 3 LLM calls minimum
async def agent_step(message: str):
    # Step 1: Classify intent (live model — measure this call)
    intent = await llm.classify(message, options=["question", "complaint", "request"])

    # Step 2: Route to handler (live model — measure this call)
    handler = await llm.route(intent, context=get_context())

    # Step 3: Generate response (live model — keep when generation is needed)
    response = await llm.generate(handler.prompt, context=handler.context)

    return response

Three serial model calls make the loop inherit the sum of their latencies and retry behaviour. Trace each call at p50, p95, and p99 before deciding which one owns the budget.

Your users are waiting.

Which Decisions Can Be Compiled?

Not every LLM call in your agent is the same. Some genuinely need the full reasoning power of a large language model. Others are doing the same classification over and over with the same set of options.

Here's how to tell the difference:

| Signal | Compilable | Needs Full LLM | |--------|-----------|----------------| | Fixed set of output options | ✅ | | | Structured input (JSON, form data) | ✅ | | | Same decision made thousands of times | ✅ | | | Decision logic is well-understood | ✅ | | | Requires multi-step reasoning | | ✅ | | Output is free-form text generation | | ✅ | | Novel situations every time | | ✅ | | Needs access to live external context | | ✅ |

In most agent architectures, Steps 1 and 2 are compilable. Intent classification and routing are structured decisions with fixed options. Step 3 — generating a response — genuinely needs the LLM.

The Fix: Compile the Repetitive Decisions

The pattern is straightforward: use an LLM to teach the decision once, then use a compiled model to make it millions of times.

For the agent loop above, you'd compile the intent classification and routing decisions into sub-100ms classifiers, and keep the LLM only for response generation:

import httpx

SPARKIENT_API = "https://api.sparkient.ai/api/v1"
HEADERS = {"Authorization": "Bearer YOUR_API_KEY"}

async def agent_step(message: str):
    # Step 1: Classify intent — compiled path; inspect returned latency_ms
    intent_resp = await httpx.AsyncClient().post(
        f"{SPARKIENT_API}/decide",
        headers=HEADERS,
        json={
            "decision_type": "agent-intent-classification",
            "input": {"text": message, "channel": "chat"}
        }
    )
    intent = intent_resp.json()["decision"]  # "question", "complaint", or "request"

    # Step 2: Route to handler — compiled model, ~35ms
    route_resp = await httpx.AsyncClient().post(
        f"{SPARKIENT_API}/decide",
        headers=HEADERS,
        json={
            "decision_type": "agent-routing",
            "input": {"intent": intent, "user_tier": "premium", "text": message}
        }
    )
    handler = route_resp.json()["decision"]  # "faq_bot", "human_agent", "escalation"

    # Step 3: Generate response — still needs a live model
    response = await llm.generate(handler_prompt(handler), context=message)

    return response

Before: 1.5-4.5 seconds (3 LLM calls) After: measure two compiled decisions plus the remaining generative call on the same traffic.

The change is successful only if the combined loop clears its latency budget and the compiled decisions pass the same held-out quality checks as the calls they replace.

Going Faster: Parallel Compiled Decisions

Since the compiled decisions are independent and fast, you can run them in parallel:

async def agent_step(message: str):
    async with httpx.AsyncClient() as client:
        # Run both classifications in parallel — total ~40ms
        intent_task = client.post(
            f"{SPARKIENT_API}/decide",
            headers=HEADERS,
            json={
                "decision_type": "agent-intent-classification",
                "input": {"text": message}
            }
        )
        moderation_task = client.post(
            f"{SPARKIENT_API}/decide",
            headers=HEADERS,
            json={
                "decision_type": "content-moderation",
                "input": {"text": message}
            }
        )

        intent_resp, mod_resp = await asyncio.gather(
            intent_task, moderation_task
        )

    intent = intent_resp.json()
    moderation = mod_resp.json()

    if moderation["decision"] == "reject":
        return {"error": "Message blocked", "reason": moderation["reason_codes"]}

    # Only call the LLM for response generation
    response = await llm.generate(route(intent["decision"]), context=message)
    return response

Now your agent does intent classification and content moderation in parallel in ~40ms, then only calls the LLM for the part that actually needs it.

MCP Integration: Agents That Create Their Own Decision Types

If your agent framework supports MCP (Model Context Protocol), your agent can create and train decision types directly — no dashboard needed. Sparkient's MCP server exposes tools for the full lifecycle:

// Claude Desktop / Cursor MCP config
{
  "mcpServers": {
    "sparkient": {
      "url": "https://mcp.sparkient.ai/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY"
      }
    }
  }
}

With this configured, an AI coding agent can:

1. Create a decision type using create_decision_type — define the name, options, and description
2. Generate training examples using generate_examples — Sparkient's LLM teacher creates synthetic data
3. Train the model using train_model — kicks off async training with a chosen preset
4. Make decisions using make_decision — call the compiled model at sub-100ms

Here's what that looks like in practice. An agent working on a support ticket system might reason:

"This codebase routes tickets by calling GPT-4o on every request. The routing options are always the same: billing, technical, account, general. I'll create a compiled decision type for this."

The agent then calls create_decision_type with name: "ticket_routing", options: ["billing", "technical", "account", "general"], followed by generate_examples and train_model. Once trained, compare the classifier with the existing call on held-out tickets and full-loop latency.

The Audit: Find Your Compilable Decisions

Here's a quick way to audit your agent's decision points:

1. Log every LLM call your agent makes for 24 hours
2. Categorize each call: Is it classification, routing, extraction, or generation?
3. Check the output space: Does it always return one of a fixed set of options?
4. Count frequency: How many times per day is this exact decision type made?

Any recurring classification or routing call with fixed options is an evaluation candidate when quality, latency, cost, privacy, or reliability creates a real constraint.

Sparkient's public benchmarks provide a starting prior, not a guarantee: four controlled synthetic domains report 0.886–0.951 macro F1 and 33–42ms batch-average time per item. Compare the exact agent call before claiming parity, savings, or a speed multiplier.

When to Keep the Full LLM

Be honest about what compilation doesn't do well:

• Open-ended generation — writing emails, crafting responses, summarizing documents. These need the LLM.
• Novel reasoning — decisions your system has never seen before, with no pattern to learn from.
• Context-heavy decisions — where the decision depends on a large, changing context window (full conversation history, live database state).

The sweet spot for compilation is the boring, repetitive, structured decisions that your agent makes thousands of times with the same options. Those are the ones burning your latency budget.

FAQ

Q: How accurate are compiled models compared to the full LLM? Across support triage, content moderation, gaming chat, and marketplace review, Sparkient's controlled synthetic runs report 0.886–0.951 macro F1 with 33–42ms batch-average time per item. That does not guarantee parity with the LLM in your project; run a held-out comparison. When enabled, optional LLM escalation can handle low-confidence cloud decisions at a different latency and credit cost.

Q: Can I use this with LangChain or CrewAI? Yes. Both frameworks support custom tool definitions. Define a tool that calls Sparkient's /decide endpoint, and the agent will use it like any other tool. For MCP-compatible frameworks, add the Sparkient MCP server directly to your config — no custom tool code needed.

Q: How much training data do I need? No historical customer dataset is required to start. Sparkient generates synthetic candidate examples from the decision definition, so the model is still trained on data. Review coverage, add representative examples when available, and test on a held-out set. A training run costs 2,000 credits.

Q: What if my agent's decision types change over time? Retrain. If you add a routing option or the decision policy shifts, update or generate examples, train, evaluate, and deploy a new version. Duration depends on the data and configuration; the existing deployed model can continue serving until you approve the replacement.

If a repeated agent decision is consuming the loop's latency budget, start with the free tier and compare one compiled candidate against the existing call.