When an AI Agent Behaves Differently Each Time: How to Improve Consistency in LLM Agents Without Choking Creativity

There's a moment that everyone who has seriously played with large models – LLMs – knows well. You run the same prompt twice, three times, five times. The first time the AI agent answers you like an experienced consultant, calm, organized. The second time – as if its personality changed, as if a first-day intern arrived. The third time it's brilliant, but skips half the requirements. Seemingly, this is "normal" when working with probabilistic models. In practice, when trying to build a real system, product, business process on this – it's simply a headache.

This article doesn't come to "explain what ChatGPT is", but to dive into the more annoying and more critical layers of working with an AI agent based on LLM: how to improve Consistency between runs, what can we even expect from it, and where we need to stop and say: "Enough, the advantage in creativity isn't worth the instability".

The Paradox of the Modern AI Agent: Flexible, Smart, Unpredictable

One of the strange things in the conversation about artificial intelligence today is the gap between the illusion and day-to-day. In marketing they talk about an AI agent "like a new employee", "like an analyst available 24/7". In practice, if a new employee gave one answer on Sunday and a completely different answer – despite the exact same request – they probably wouldn't make it past the first month. But when this happens with a large language model, we tend to forgive, call it "stochastic", "creative".

The reason is quite deep. An LLM is not deterministic software in the classical sense. Even if we lower its temperature to 0, even if we do every known trick, there are still elements of uncertainty. Add to that the AI agent layer – that agent that manages operations, calls APIs, chains prompts, maybe consults with several models – and you've got a system with many points where things can "escape" in different directions.

But Why Do We Care About Consistency Anyway?

Let's put emotion aside. Consistency is not an aesthetic matter, it's a condition for two basic things:

On one hand, user trust. If a product manager decides to build an internal tool that helps the sales team, and discovers that the AI agent produces completely different quotes based on the same assumptions – there's no tool here, there's a lottery. On the other hand, testing and verification capability. How do you test system quality when every run gives a different result? How do you compare variants? How do you verify that a fix we made didn't cause damage elsewhere?

And that's before we've touched on regulation, reportability, systems where documentation is critical. This is exactly where techniques for improving consistency between runs in LLM agent worlds come in.

What Even Counts as "Consistency" in the Era of Probabilistic Models?

Before running to solutions, we need to define – even to ourselves, on a napkin – what Consistency means in the context of an AI agent. It doesn't always mean the verbal answer will be identical bit-by-bit. The world of language is too natural for that.

Consistency at the Result Level, Not Necessarily at the Text Level

When we talk about consistency, there are usually at least three layers:

1. Logical Consistency

If an AI agent is required to answer a factual question – for example "what is the VAT percentage in Israel currently?" – we expect to get the same number (assuming nothing changed in the real world) every time. If one time the model answers 17% and another time 18%, we have a problem.

2. Procedural Consistency

Here we're talking about process: how the AI agent decides to act. When we use an LLM agent designed as an "agent" – one that chooses tools, calls systems, executes thought chains – we want the basic path to be similar: the same tools for the same scenarios, the same answer structure, more or less. Even if the phrasing changes.

3. Stylistic and Scope Consistency

This is already an experiential matter. Users get used to an AI agent's style – answer length, number of examples, level of caution. If in every run the model suddenly "decides" to speak at a different length or in a different tone, the feeling is that there's no polished product here, but a continuous demo.

Our goal, when we design serious LLM agents, is to create a system that maintains consistency in all three layers – but without turning the model into a dry robot that can't improvise in the right places.

The Basic Tactics: Prompts Are Not Magic, They're a Work Contract

Let's start from the most known area – prompting – but try to talk about it less as a marketing trick and more as an engineering tool. Those who built a production AI agent know: a good prompt is a kind of work contract between the system and the model.

Identity and Style Reset: "Remember Who You Are" in Every Run

Not a little inconsistency is born from the model "forgetting" who it is and what was expected from it. Yes, even if it sounds too human. The relatively simple – but critical – solution is a stable System prompt, that repeats the principles of identity, purpose, and boundaries of the AI agent in every interaction.

For example (in free translation, without pretense to perfect style): "You are an AI agent that helps finance managers in small companies in Israel. You always give focused answers, with numbers. If you don't have enough information, you say so explicitly and don't guess".

Sounds trivial? In practice, many systems break here. Every sudden change in the system prompt, every "small experiment" in production – can turn consistency into a distant dream. Therefore one of the basic tricks is to treat the main prompt as code – with version control, A/B testing, and change documentation.

Maintaining a Fixed Answer Template – Especially for Multi-Tool AI Agents

When it comes to LLM agents that return answers to other systems (and not directly to the user), consistency in answer structure is even more important than content. A small change in JSON structure, a field that disappeared, a field that became a list – and suddenly half the pipeline explodes.

Therefore, a very effective technique is to work with a rigid format:

• Always require fixed fields (status, reasoning, actions, final_answer).
• Remind in every prompt again of the expected answer structure.
• Sometimes – add a validation layer that fixes or re-prompts if the answer doesn't meet the format.

All this might sound tedious, but an AI agent that works with payment systems, CRM or BI simply must have this level of consistency so we don't spend nights debugging instead of building.

Controlling Randomness: Temperature Is Not a Toy

There's a tendency to treat parameters like temperature, top_p and such as if they were style buttons. "Let's raise it a bit to 0.9, make more creative answers". In practice, for those seeking Consistency, this is one of the first places to look at seriously.

When to Freeze, When to Release

Practically, when building an AI agent that has "creative" parts and "regulatory" parts, you can – and even should – play with temperature values within the flow:

• Logic, calculations, tool selection → very low temperature (0 to 0.2).
• Marketing text phrasing, ideas, brainstorming → medium temperature (0.5–0.7).

The same LLM agent can within the same conversation switch between different "states of consciousness" – suppressing randomness when precision is needed, relative release when inspiration is needed. Those who don't exploit this, usually either choke the system, or get inconsistency at the most critical junctions.

Seed and Controlled Randomization

There are platforms that allow setting a seed for the model run, to try to reproduce answers. This sounds tempting – "we'll set a fixed seed, always get the same answer" – but in the real world it's a bit more complex: a small change in the prompt, in a hidden field, in the model version – breaks the illusion.

And still, in testing and development systems, using a seed can help a lot to understand if a change in the AI agent's wrapper code affects behavior, or if the change comes from the model itself. It's an important debugging tool, even if not a magic solution for consistency in production.

Thought Chains, Memory, and When It Actually Interferes with Consistency

One of the obvious trends in the AI agent field is to allow the model to "think out loud" – Chain of Thought, ReAct, all the nice names. The model writes reasoning for itself, decides on actions, checks results, and so on. It's amazing, when it works. It's also a huge source of inconsistency.

Chain of Thought: An Algorithm That Invents Its Own Path Anew

When we let an LLM formulate its own path to a solution, it won't necessarily choose exactly the same way in different runs. Sometimes this is good – because it can find a smarter solution – but when it comes to a system that needs to look stable, there's a price.

One technique to get "the best of both worlds" is to maintain a kind of template logic. For example, allow the AI agent to think out loud, but require it to follow fixed steps:

1. Understanding the question and context.
2. Checking relevant information (including documented API calls).
3. Synthesizing the information.
4. Formulating a final answer in the agreed format.

Even if the content of the reasoning changes, the fact that the model "thinks" in a consistent pattern greatly improves Consistency at the process level.

Long-Term Memory: Blessing or Curse for Consistency?

Another layer of complexity is born when adding long-term memory to an AI agent – between conversations, between runs, between users. In the Israeli scene, more and more startups are trying to build "permanent" agents, ones that remember previous conversations, loaded documents, the client's work routine.

Seemingly, memory should improve consistency – because the system learns the user. In practice, if memory isn't managed correctly, it causes the opposite effect: the same request gets a different answer because once a certain detail was mentioned a month ago, and another time it wasn't.

The solution? Store memory in a structured way, with clear policy:

• What counts as a "fixed fact" that always enters the prompt.
• What counts as a "preference" that's weighted, but not allowed to change business logic.
• How to delete or update memory when wrong.

In other words: memory needs to be managed like a database, not like an open notebook.

State Management in LLM Agents: Behind the Scenes of Consistency

In the old world, before we started talking about AI agents, "state" was a clear matter: variables, objects, session. Today, part of the state lives in the prompt, part in code, part in the database, and part – in the linguistic arbitrariness of the model.

Separation Between Application State and Linguistic State

One of the common mistakes is to mix everything: both business settings, conversational context, tool settings – all crammed into the same prompt. This might work at first, but almost inevitably leads to inconsistency the moment the system grows.

An effective technique is to separate:

• Business State – stored in an external system (DB, Redis, whatever), and injected into the prompt selectively.
• Linguistic State – the conversational history itself, stored in a modest format, maybe summarized.
• Meta-State – decisions about system state, like "is the user in advanced permissions", "is this an A/B test".

When this separation is done well, you can ensure the AI agent receives in every run the same operational foundation for the same request, and that's already a huge step toward Consistency.

Where This Meets Israel: Between Startup Nation and a Client Expecting Stability

In Israel there's a special kind of dissonance. On one hand, we're a country that celebrates experiments, MVP, "let's launch and see". On the other hand, many of the hottest uses for AI agents come from very unforgiving worlds: fintech, digital health, GovTech, legal services.

I've heard from a young Israeli venture, working on an LLM agent for finance departments in organizations. They started small – an internal tool that helps analyze Excel files and answer questions. After a few pilots, the main client told them simply: "I'm willing to tolerate 10% less accuracy, but not willing for it to throw an error one time and not another". In other words: prefer less smart but more consistent.

This is perhaps the most Israeli – and most practical – insight around LLM agents: in the end, managers want to know where the ceiling is. Not everyone gets excited that the system "surprises them for the better" if sometimes it also surprises for the worse. Consistency is perceived not as a technical parameter but as a character trait of the product.

AI Agent as a Permanent Guest in the Organization: Work Processes Around Consistency

Until now we've talked mainly about the technical side. But Consistency in AI agent systems depends no less on organizational processes. The way changes are managed, expectations, communication with users.

Version Control Not Just for Code – Also for Prompts and Models

If there's one sentence that LLM agent developers need to hang in front of them, maybe it's: "A prompt is code". Every change in the text fed to the model – even a small phrasing change – can have an effect. Sometimes for the better, sometimes for the worse, and often simply break consistency.

Therefore, a professional process will include:

• Saving all versions of system prompts and tool prompts.
• Running a fixed test set (test prompts) after every change.
• Organized documentation of "what changed and why".

Those who work this way discover that suddenly they have a language to talk about Consistency – not just gut feeling.

Transparency to Users: "This Isn't a Rigid Bot, It's a Learning System"

Another point worth raising – especially in the Israeli, direct market – is the level of transparency. Maybe not in every consumer product, but in advanced B2B systems there's real value in explaining to users how the AI agent works, what its boundaries are, and what's expected from it.

When you set a realistic expectation – "answers can vary slightly between runs, but the business result should be the same" – it's much easier to manage the conversation about Consistency. Without this, every small deviation feels like a betrayal of the original promise.

Frequently Asked Questions About Consistency in AI Agents

Can You Make an AI Agent Always Answer Exactly the Same Thing?

In most cases – not fully, and not worth forcing. You can bring the system closer there by lowering temperature, hardening formats and managing State, but language models are meant to be flexible. The realistic goal is consistency at the logic and result level, not necessarily at the exact words level.

Why Does an AI Agent Sometimes "Forget" Explicit Instructions We Gave It?

Usually this happens for a completely technical reason: the conversational history gets long, parts of the prompt get cut, or the strict instructions were embedded too deep in the text and didn't get priority. Proper use of system prompt, together with reducing noise and clear prompt structure, significantly reduces the phenomenon.

Does Using Several Models in Parallel Harm Consistency?

It can harm – but doesn't have to. If you clearly define which model is responsible for what (logic, information, phrasing), and maintain clear boundaries between agents, you can achieve a system where multiplicity actually strengthens consistency – for example through cross-check between two AI agents. Without such discipline, it quickly becomes an unpredictable circus.

How Do You Measure Consistency Practically?

One of the simple tools is to build a collection of stable "test prompts", run them again and again (both after model upgrade, and after prompt change), and check deviations – in result, in answer structure, in tool usage. You can measure deviation percentage, rank their severity, and define an acceptance threshold.

What's the Biggest Risk from Lack of Consistency in LLM Agents?

Beyond damage to trust, the central risk is in making wrong decisions – especially in sensitive fields. If one time an AI agent recommends acting one way and another time another way, without a transparent change in background conditions, professionals can lose their sense of direction. Therefore, in any field with financial, legal, or medical implications – Consistency is not a "bonus", it's a fundamental requirement.

Summary Table: Key Techniques for Improving Consistency in LLM Agents

Aspect	Common Problem	Techniques to Improve Consistency	Implementation Notes
Prompts and AI Agent Identity	Sudden behavior changes between runs	Stable system prompt, change documentation, defining a clear "contract" with the model	Treat prompt like code: version control and testing
Answer Structure	Changing JSON, missing fields, breaking integrations	Requiring rigid format, automatic validation, re-prompt in case of failure	Critical especially in AI agents that talk to other systems
Randomness (temperature etc.)	Too different answers to the same question	Lowering temperature in logical tasks, dynamic use of values by stage	Can leave creativity only where it adds real value
Chain of Thought and Reasoning	Changing solution paths, hard to reproduce	Defining fixed steps, maintaining consistent reasoning pattern	Also allows easier debugging, not just consistency
Memory and State	Different answers due to "old" or missing memory	Separating business State from linguistic, structured memory management, update and deletion	Think of memory like a DB, not like a personal diary
Combining Several Models / Agents	Unpredictable behavior due to multiple sources	Clear definition of each AI agent's responsibility, use of orchestration	Can benefit from mutual checking, but need to bound well
Organizational Processes	Inconsistency following "quiet changes" in production	Organized Release processes for prompts and models, regression tests	More DevOps, less "let's try on the client and see"

Where This Is Going: From "Cute Chatbot" to AI Agent That's Part of the Team

If we stop for a moment and think ahead, we'll see that the LLM agent world is going in a pretty clear direction: less gadget, more infrastructure. When an AI agent becomes an integral part of a team – whether it's a "legal assistant" in a law firm, a "clinical helper" for a family doctor, or a "shadow analyst" in a finance department – the central question won't just be "how smart is it", but "how much can we trust it".

Consistent behavior – predictable, transparent, explainable – is the foundation of that authority. This doesn't mean we'll turn the models into tie-wearers without a sense of humor, but it does mean we'll learn to draw a line: where to let the AI agent wander, and where to anchor it to the floor.

The path there passes through both technique – everything we talked about around prompts, State, randomness – and worldview. To understand that a language model is a somewhat strange partner: very smart, but not deterministic. To live with it in peace, you need to set frameworks for it. Not out of fear, but out of responsibility.

A Final Word: If You're Building a Serious AI Agent – Don't Stay Alone with It

If you've reached here, you're probably not looking for another chatbot for fun, but trying to introduce an AI agent into real processes – in an organization, in a product, in a startup that needs to stand the test of reality. In such a situation, questions about Consistency are not marginal, they're the heart of the matter.

Every organization, every field, and every type of LLM agent requires a slightly different combination of the techniques we laid out here. Sometimes the solution is simply lowering temperature and hardening prompts, sometimes you need to redesign the entire State and memory flow, and sometimes – to admit that the current use isn't suitable for a probabilistic model without an additional control layer.

If you're debating how to approach this – how to build a consistent, reliable AI agent that doesn't drop you with a "creative" answer at the most sensitive moment – we'd be happy to help with an initial consultation at no cost, simply to help focus the right questions and save you from some of the known minefields in advance.