Designing Agentic Systems Like Distributed Systems

Agentic development is rapidly becoming one of the most talked-about paradigms in software development. The talk is not just of using AI to assist in coding but of using systems where an AI agent is capable of planning, executing tasks, and even deciding.

From a surface-level perspective, agentic systems are a new abstraction. But if we look under the hood, we find something that looks rather familiar: distributed systems.

In microservices, asynchronous workflows, or event-driven architectures, many of the same challenges apply:

• Irregular behavior
• Partial terminal conditions
• Latency fluctuations
• Lack of observability

The biggest mistake teams make is treating agents like deterministic scripts. In reality, they require the same rigor and design discipline as distributed systems.

The Illusion of Determinism

The traditional software model is fundamentally deterministic. Under similar conditions, one expects the same result.

Agentic systems contradict this assumption.

Identical prompts and inputs cannot always cause the same outputs because of:

• Model variability
• Context variation
• Token limits
• The response from an external tool

This is akin to the behavior of distributed systems that have to deal with the real-world conditions - network latency, retries, and service dependencies that generate differences.

This logically means that you cannot rely on "it worked once" as proof of correctness. 

Instead, you must design for:

• Variability
• Approximation
• Probabilistic correctness

This one modification is sufficient to prompt engineers to reconsider the entire approach to achieving reliability.

Agents Are Just Services With Unstable Contracts

In the realm of distributed systems, services often interact with clearly defined contracts. This is usually an API, schema, or a versioned interface.

However, the converse is often true for the agentic systems.

A typical agent flow might look like:

• Create a response
• Call a tool
• Parse the output
• Decide on the next Action

However, without strict contracts things break:

• The model returns JSON that is not entirely the same
• There is a field that is either missing or has been renamed
• The tool response format is different

These problems are not edge cases; they are expected behaviors.

The solution is to treat agents like services with stricter contracts:

• Ensure that the outputs are structured clearly (JSON schemas, typed responses)
• Validate each interaction that takes place
• Fail fast on invalid responses

You don't trust the model, you would rather encase it in a construct that ensures correctness at the boundaries.

Orchestration Over Autonomy 

There is a general perception that agents are autonomous and can thus operate independently.

In reality, this is not often the case in production scenarios.

What actually works is orchestration.

Like the distributed systems that make use of orchestrators (workflow engines, schedulers, queues), agentic systems also require:

• Feedback control loops
• Stepwise execution
• Explicit state transitions

The robust agentic workflow includes the following main steps:

1. Propose the task
2. Implement a single step
3. Check output
4. Choose the next step
5. Loop or terminate

This is not autonomy, but rather controlled implementation.

It’s a bit like a state machine rather than a self-driving system.  

The more critical the workflow, the more you need control:

• Limiting agent freedom
• Specifying allowed actions
• Adding human-in-the-loop checkpoints when needed

Without a doubt, orchestration is what makes systems reliable, though autonomy does have its own charm.

Failure Is the Default State 

Distributed systems are frequently structured in the same way. Thus, failure is not a special event but, rather, a normal occurrence.

This holds true even for the agentic systems; thus, failure is a possibility.

Errors can arise on different fronts:

• The model might misjudge what the issue actually is
• A tool call could fail or timeout
• The agent might get stuck in a loop
• The output is syntactically correct but semantically wrong 

If your system assumes success, it will fail in production.

Rather, design for failure such as:

• Adding retries with limits
• Implementing timeouts
• Introducing fallback paths
• Detecting and breaking infinite loops

For example:

• If the agent is unable to produce valid output for 3 repeated attempts, it will flow to a deterministic flow
• If a tool call fails, it can still give a degraded yet safe response

This shows the circuit-breaker and retry policy patterns at work in distributed systems.

Reliability comes not from avoiding errors but from handling errors gracefully.

Observability Is Non-Negotiable 

One of the hardest issues in distributed systems is observability, or understanding what happened when something has gone wrong.

But in agentic systems, it is ever harder

Why?

The answer is that failures are often not binary.

The system could:

• Deliver an answer that's covertly erroneous
• Use the wrong reasoning
• Adopt incorrect assumptions

Without observability, debugging will be guesswork.

Application of agentic systems in production thus needs:

• Structured logs of every step
• Prompt and response tracing
• Tool invocation tracking
• Path decision visibility

Think of it as distributed tracing for agents.

Instead of just logging outputs, log:

• Inputs
• Intermediate reasoning (if safe)
• Tool calls and results
• Final decisions

This allows you to answer critical questions: 

• Where did the system go astray?
• Was it the model, the prompt, or the tool?
• Is that an isolated issue, or is it a pattern?

Good observability changes the unpredictable systems into manageable ones. 

Idempotency and State Management 

In distributed systems, idempotency guarantees that repeated actions don't produce unintended consequences.

Agentic systems need this even more.

Consider the scenario where:

• A step is retried
• A tool is called multiple times
• The agent restarts mid-flow

These situations will lead to some of the following outcomes:

• Twice the number of actions
• Outputs that are inconsistent
• Workflows that are corrupted

Best practices include:

• Keep the explicit state stored between steps
• Make tool calls idempotent where possible
• Keep a track of execution history

For example:

Rather than allowing the agent to "remember" context implicitly, persist:

• What steps were completed
• What outputs were produced
• What decisions were made

This will turn a brittle state into one that is recoverable.

Guardrails Over Intelligence 

One common misconception is that improving the model will solve most problems.

However, system design matters more than model capability.

More robust models mean fewer mistakes, but they do eliminate:

• Ambiguities
• Misinterpretations
• Unexpected outputs

Guardrails are what make systems usable:

• Input validation
• Output constraints
• Action limits
• Safety checks

For example:

• The agent can only call the tools that are allowed
• Validate outputs before execution
• Destructive actions must be prevented

This resembles the way in which distributed systems enforce:

• Access controls
• Rate limits
• Data validation

You don’t trust components blindly; rather, you constrain them.

Closing Thoughts

Agentic development is not about replacing the engineering discipline. It is about rigor in applying it.

The most effective systems are not necessarily the most independent. They are the ones that are:

• Intelligently orchestrated
• Heavily constrained
• Deeply observable

Ultimately, the agents are simply another layer in your architecture.