LLMs as Semantic Middleware 3/3 — Between Humans and Systems

The Same Problem, One Layer Up

Every complex system eventually runs into the same issue: translation. As systems grow and teams expand, meaning starts to leak. The interfaces between humans and systems behave much like those between services — brittle, lossy, and easily overloaded.

In software, we handle this through middleware. It intercepts requests, enriches them with context, and ensures that data reaches its destination in a usable form. Without it, systems fragment under their own complexity.

Inside organizations, people already play a similar role, though we rarely describe it in those terms. I've watched this pattern repeat across every team I've led or advised: someone is always translating between layers that don't naturally align.

Humans as Middleware

Most roles in a company exist to connect incompatible layers.

• Product managers bridge user intent and engineering execution.
• Tech leads connect architectural direction with delivery.
• Analysts turn data into decisions.
• Designers translate user perception into behavior and interface.

Every one of these roles moves meaning across a boundary that does not naturally align. They interpret, reformulate, and adapt information so that different parts of a system can cooperate.

This is human middleware in action.

The Cost of Translation

I once led a team where a single product manager spent 60% of their time in translation: turning customer feedback into feature specs, translating engineering constraints back to stakeholders, and aligning roadmaps across three different teams. They weren't lazy or inefficient—they were doing the work the organization needed to function.

But human middleware has limits:

• Context is expensive to hold: each person can only keep so much in their head.
• Transfer is slow: explaining context takes time, and it degrades with each handoff.
• It doesn't scale: as organizations grow, the amount of information that needs to move grows faster than the ability to carry it.

At a certain point, the cost of coordination exceeds the benefit of specialization. Teams start losing coherence not because of lack of skill, but because of lack of shared meaning.

Extending the Middle Layer

Large language models can strengthen this middle layer without replacing it. They make translation explicit, measurable, and reviewable.

In practical terms, an LLM can:

1. Summarize and structure — turning long, unstructured discussions into clear context that can be reused.
2. Translate dialects — aligning language between domains, such as product, engineering, and business.
3. Detect drift — surfacing when two teams describe the same thing differently.

These are all forms of semantic middleware. The LLM doesn't create knowledge; it maintains coherence. It ensures that intent and interpretation stay close enough for the system to function as one.

Example: Meeting Notes That Actually Work

At one company, we had a recurring problem: after every planning meeting, someone had to write a summary. It took 30 minutes, was often incomplete, and nobody read it carefully. By the next meeting, half the team had forgotten the decisions.

We introduced a lightweight LLM layer:

# After each meeting, transcribe and summarize
def summarize_meeting(transcript: str) -> MeetingSummary:
    prompt = f"""
    Summarize this meeting in three sections:
    1. Decisions made (what we agreed to do)
    2. Open questions (what we need to resolve)
    3. Action items (who does what by when)
    
    Format as markdown. Be specific.
    
    Transcript:
    {transcript}
    """
    
    response = llm.complete(prompt)
    
    return MeetingSummary(
        text=response.text,
        transcript_source=transcript,
        generated_at=now()
    )

The result wasn't perfect, but it was good enough to review and refine in 5 minutes. More importantly, it created a shared artifact: a single source of truth that everyone could reference.

Suddenly, alignment was easier. Instead of "I thought we decided X," people could point to the summary and say, "Here's what we agreed."

Translating Between Domains

One of the most painful translation layers in any organization is between technical and non-technical teams. Engineers think in systems, constraints, and trade-offs. Product and business teams think in outcomes, user needs, and timelines.

I've seen this break down repeatedly: an engineer says "we can't ship that without refactoring the auth layer," and a PM hears "we don't want to do this." A PM says "customers need this urgently," and an engineer hears "ignore technical debt and hack something together."

Neither is right. Both are translating poorly.

Example: Technical Explanations for Non-Technical Stakeholders

One engineering lead I coached struggled to explain architectural decisions to their CEO. Every conversation ended in frustration: the CEO thought the team was moving too slowly, and the team thought the CEO didn't understand the constraints.

We built a simple LLM translator:

def explain_for_non_technical(technical_explanation: str) -> str:
    prompt = f"""
    Translate this technical explanation for a non-technical executive.
    Focus on:
    - Business impact (time, risk, cost)
    - What happens if we do this vs. don't do this
    - Clear trade-offs
    
    Avoid jargon. Use analogies if helpful.
    
    Technical explanation:
    {technical_explanation}
    """
    
    response = llm.complete(prompt)
    return response.text

Before:

"We need to migrate to a distributed tracing system with OpenTelemetry support to improve observability across microservices."

After:

"Right now, when something breaks, it takes us 2-3 hours to figure out which service caused the problem. This upgrade will cut that to 15 minutes, which means faster fixes and less downtime. It'll take 3 weeks to implement."

The CEO understood immediately. The team got approval. The LLM didn't make the decision—it clarified the trade-off.

Detecting Semantic Drift

As teams grow, language drifts. One team calls a concept "customer," another calls it "account," a third calls it "user." Everyone thinks they're talking about the same thing, but they're not.

I've seen this cause weeks of wasted work: two teams build features for different definitions of "user," then discover they're incompatible.

Example: Terminology Alignment Across Teams

At a fintech company, we had four teams using the term "transaction" to mean four different things:

• Payments team: a money transfer between accounts.
• Analytics team: any user action in the app.
• Compliance team: a reportable financial event.
• Engineering team: a database transaction.

This caused constant confusion. Meetings turned into debates about definitions.

We built a simple LLM-powered glossary checker:

def check_terminology_drift(document: str, team: str) -> list[Warning]:
    prompt = f"""
    Identify terms in this document that may have different meanings across teams.
    Flag terms like "transaction," "user," "account," "session."
    
    Document: {document}
    Team: {team}
    """
    
    response = llm.complete(prompt)
    return parse_warnings(response.text)

When someone wrote a spec that said "we'll track transactions per user," the system flagged it:

⚠️ Warning: "transaction" may mean different things to Payments and Analytics. Clarify which definition you mean.

This didn't solve the problem automatically, but it surfaced the ambiguity before it caused a misalignment. Teams started being more precise in their language.

Research and Precedent

Research already points in this direction:

• Towards a Middleware for Large Language Models and Middleware for LLMs: Tools Are Instrumental for Language Agents explore architectures where models act as connectors between systems and meanings.
• Exploring Human-Like Translation Strategy with Large Language Models shows that LLMs decompose and recompose meaning much like human translators.
• Intersubjective Communication via LLMs suggests that models can help align shared understanding across different perspectives.

Taken together, these ideas describe the same pattern: LLMs are at their best when placed between things that misunderstand each other.

Designing the Human–Model Flow

Treating LLMs as semantic middleware means designing them like we design reliable systems. The same principles apply:

• Observability: keep a record of what was translated and how.
• Versioning: evolve prompts and templates like APIs.
• Graceful degradation: ensure the process still works when the model fails.
• Clarity of contract: define exactly what the model owns and what remains human.

This framing shifts the conversation away from automation and toward coherence. A healthy organization — like a healthy system — keeps meaning intact as it moves through layers.

A Practical Pattern: Human-in-the-Loop Middleware

The best LLM integrations I've seen don't replace humans—they amplify them:

def translate_with_review(input: str, context: str) -> Translation:
    # LLM does the first pass
    draft = llm.translate(input, context)
    
    # Human reviews and refines
    reviewed = human_review(draft, input, context)
    
    # System learns from corrections
    if draft != reviewed:
        log_correction(input, draft, reviewed)
        update_prompt_weights(input, reviewed)
    
    return reviewed

This creates a feedback loop: the LLM gets better over time, and humans spend less time on routine translation. The model handles the 80% case, humans handle the nuance.

Try This

Pick one recurring communication bottleneck in your organization:

1. Meeting summaries that nobody reads.
2. Cross-team specs that get misinterpreted.
3. Technical explanations that stakeholders don't understand.

Add a simple LLM layer:

1. Capture the input: meeting transcript, spec draft, technical explanation.
2. Define the output format: decisions + action items, disambiguated terms, business-impact summary.
3. Generate a draft: use the LLM to produce a first pass.
4. Human review: refine the draft, correct errors, improve clarity.
5. Track corrections: log where the LLM succeeded and where it needed help.

Run this for two weeks. You'll start to see patterns: what the LLM handles well, where it struggles, and how to improve the prompts. More importantly, you'll see whether the translation layer actually reduces coordination overhead.

That's how you turn organizational ambiguity into operational clarity.

Final Reflection

Middleware for meaning is as necessary between humans as it is between code.

When every part of an organization can understand what the next one means, translation stops being a source of friction and becomes part of the design. Transparency flows naturally. Alignment happens faster. Trust builds because context is preserved.

LLMs don't replace the humans doing this work—they make the work scalable. The model handles routine translation; humans handle judgment, nuance, and trust.

When you treat LLMs as semantic middleware, you stop asking "can AI replace this?" and start asking "where is meaning getting lost, and how do we preserve it?"

That's a better question.

Sources

• Towards a Middleware for Large Language Models (arXiv)
• Middleware for LLMs: Tools Are Instrumental for Language Agents (arXiv)
• Exploring Human-Like Translation Strategy with Large Language Models (MIT Press)
• Intersubjective Communication via LLMs (arXiv)