Pipeline AI vs agentic AI for code reviews: Let the model reason — within reason

AI has changed what code reviews can be.

We’ve gone from static rules and regex-based linters to systems that can actually read a diff and respond with feedback that resembles what a senior engineer might say. That’s real progress.

But as companies like CodeRabbit create production-grade systems for code reviews or for other developer-focused tools, we all face a core architectural question:

Do you give the AI autonomy to plan and act like an agent? Or do you structure the process as a predictable AI pipeline?

This choice affects more than just implementation. It shapes how fast your system runs, how much developers trust it, how you debug it when it breaks, and what it takes to maintain it long-term.

And while the architecture matters, it's not the end goal. These are just different ways of trying to answer the same underlying question —

How do we give the model everything it needs (and nothing more) to deliver the best code review possible?

That’s the real challenge. Not "agentic AI" vs. "pipeline AI." Just building the best possible tool for the people who use it.

We’ll come back to that. But first, let’s define the two camps.

AI architectural patterns: Agentic AI vs. pipeline AI

Agentic AI systems

In an agentic architecture, the model isn’t locked into a single prompt. It’s allowed to think step-by-step, make decisions, and use tools as it goes. Often this means:

• Planning a course of action

• Calling a tool (e.g. grep, a static analyzer, test runner)

• Observing the output

• Deciding what to do next

This approach — often referred to as ReAct (Reason + Act) — is one of several reasoning patterns used to guide agent behavior.

It shows up across a range of modern systems and research prototypes, but the core idea is the same: the model can reason, act, observe, and repeat — using external tools and memory to enrich its output. That flexibility is incredibly promising.

It’s also incredibly hard to get right.

Pipeline AI systems

Pipeline AI-based systems take a more deterministic approach. You define a sequence:

1. Prepare inputs (e.g. diff, relevant file slices, issue text)

2. Run pre-processing (e.g. static analysis, code search)

3. Call the model with a crafted prompt

4. Post-process the output into review comments

This approach is predictable, fast, and easy to test. It’s also easier to integrate into CI workflows, where speed and reproducibility matter.

Many tools use a pipeline AI backbone as their foundation, however, most modern implementations also incorporate elements of agentic behavior. They may dynamically adjust prompts, use retrieval strategies, or support interactive review flows.

They aren’t fully agentic, but they aren’t rigidly linear either.

Which brings us to the reality most teams face: you don’t have to pick a side. Most real-world systems live somewhere in the middle — not for philosophical reasons, but because that’s what it takes to ship something reliable, adaptable, and useful.

Hybrid AI systems: A spectrum, not a binary

In practice, many real-world systems don’t land fully in either the agentic AI or pipeline AI camp. They blend elements of both — taking the structure and reliability of pipelines, and layering in tool use, learned behavior, or context enrichment strategies that are often associated with agents.

CodeRabbit is a good example of this kind of hybrid AI architecture.

GitHub Copilot PR Reviews also falls into this category. While their interfaces and goals differ, they share similar DNA — blending structured inputs with retrieval, static analysis, and interactive flows.

We go deeper into CodeRabbit’s AI pipeline and enrichment strategy in the next section, but in short: it blends the determinism and predictability of pipelines with dynamic, learned behavior and targeted context augmentation — sitting squarely between the two paradigms.

Hybrid AI systems like this sit along a spectrum — and that's the point. You don’t have to go all-in on one paradigm. You just have to solve for what matters: helping your users make better decisions, faster, with fewer surprises.

Hybrid systems aim to balance the pros and cons of both agentic and pipeline systems by finding a balance between the two. Striking the right balance can also be difficult to achieve, with some experimentation required. This added control and flexibility can increase the cost of development and maintenance.

Tradeoffs between AI architecture patterns

Agentic AI systems offer flexibility — but flexibility is a double-edged sword. They can fetch exactly what’s needed… or fetch everything and drown in noise. They can reason step-by-step… or loop forever. You need good defaults, good tools, and often, some level of hard constraint.

Pipelines, by contrast, are stable. You get speed, control, and a well-bounded behavior space. But they can be rigid. If the context isn’t there at the start, the model can’t do much about it.

That’s the tradeoff.

And that’s what most of us are doing here — not debating abstractions, but working to build the best damn tool we can. For ourselves. For our teams. For the developers who need to ship something today.

The AI architecture pattern you use is just a means to an end. The real work — and the real leverage — lies somewhere else.

AI context is the real bottleneck: Why autonomy needs structure

More context isn’t always better

In AI code review, we spend a lot of time debating architecture — agentic AI vs. pipeline AI — but the real performance bottleneck is often upstream: what context we give the model.

There’s a common assumption:

If we just add more AI context — more code, more metadata, more analysis — the model will perform better.

But that’s not how it works.

• Too much irrelevant input overwhelms the model (Secure Code Review at Scale)

• Prompt noise leads to muddled reasoning and false positives (Secure Code Review at Scale)

• Even high-quality tools can generate low-quality AI context if used indiscriminately (Anthropic Case Study)

More isn’t better. Better is better.

Agent autonomy sounds great — but struggles in practice

Agentic systems promise flexibility: let the model decide what it needs, when it needs it, and fetch context accordingly.

In theory, this is ideal. In practice, it’s messy.

Common failure patterns:

• Tool overuse — agents calling everything, just in case (DevTools Academy)

• Redundant or noisy fetches that dilute the prompt (Prompt Engineering Guide)

• No clear reward signal to distinguish helpful context from useless output (ReTool)

Agent autonomy without structure doesn’t scale.

At CodeRabbit, we curate context — we don’t wander

We’ve taken a different approach.

CodeRabbit’s system:

• Runs 30+ static analyzers before prompting the model

• Uses AST and symbol lookups to identify relevant context

• Applies context filters based on past review learnings

• Structures inputs carefully to fit model limits and prompt constraints

This hybrid AI pipeline gives the model exactly what it needs — and nothing more. No random guesses, no runtime surprises.

We’ve learned that great reviews come from:

• Tight, relevant context

• Consistent structure

• Just enough flexibility to adapt to the code change at hand

Could agents learn to curate context?

Maybe — and that’s the interesting future path.

If we had:

• A dataset of pull requests with “ideal context sets”

• Evaluation metrics tied to actual review outcomes

• Synthetic examples showing what helps and what hurts...

...then we might be able to train agents to call tools intelligently. To act more like great reviewers than interns with shell access.

That’s the direction explored by recent work like ReTool and LeReT, which use reinforcement learning to teach agents retrieval strategies — learning which tools to invoke and when, based on feedback loops tied to downstream task quality. ReTool showed improvements in task accuracy of up to 9% over retrieval-agnostic baselines, and required significantly fewer training steps to converge. LeReT similarly demonstrated a 29% boost in retrieval success and a 17% gain in downstream QA accuracy over standard retrievers — strong early signals that agents can, in fact, learn to fetch the right context when properly trained.

But even with these improvements, we’re still lacking high-quality, domain-specific datasets for tasks like code review.

One path forward could involve curating a large-scale benchmark of real and synthetic pull requests, each labeled with:

• The issue or defect type present (e.g. logic bug, perf regression, missing test)

• The AI context types that improve or degrade LLM performance on detecting that issue (e.g. AST, file diff, related function definitions, ticket description)

• The tool invocations used (or simulated) to assemble that context

With this dataset, we could:

• Evaluate which types of PRs benefit from which types of context

• Train agents to learn context selection policies based on PR characteristics

• Create specialized sub-agents for different error classes (security, style, performance), each using context proven to enhance detection of those issues

In other words: teach agents to reason more like experts — not just by copying what they say, but by emulating how they gather, filter, and apply the information that matters.

And we wouldn't have to guess at it. We could back it up with data.

That’s the deeper opportunity: not just training agents to run tools, but to understand why and when to use them — grounded in evidence, driven by outcomes.

We’re not there yet. But the path is starting to look clearer. And at CodeRabbit, we’re leading the charge. This is exactly the frontier we’re investing in: building hybrid AI systems that can predict the right tool to use, at the right time, for the right kind of review. Not just to make something clever — but to make something teams can trust.

Our hybrid AI pipeline: We reason with purpose

By now, it should be clear that "agentic AI vs. pipeline AI" isn’t the real battle. These are just architectural tools — different shapes we use to tackle the same core problem:

How do we give the model exactly what it needs to deliver a useful review — and nothing that drags it off course?

Pipeline AI systems give us speed, control, and consistency. Agentic AI systems promise adaptability and richer reasoning. And hybrid AI systems, like what we’ve built at CodeRabbit, try to walk that line — combining structure with flexibility, precision with power.

But no matter how you structure it, one thing matters above all: context.

The hard part of code review — for both humans and machines — isn’t the format. It’s knowing where to look. What matters. What can be ignored. What’s risky. What’s surprising. That’s what great engineers learn to spot, and it’s what we’re trying to teach our models to do.

That’s the exciting part.

Because if we can train a system to not just analyze a diff, but to know which tool to call, when to call it, and how to interpret its output with surgical precision — then we’re getting closer to something remarkable.

Not just automation. But reviews that feel like they came from your best engineer — on their best day — every time.

That’s what we’re building toward. Not for the sake of cleverness, but because that’s what teams need: trustworthy tools that help them move fast, write better code, and ship with confidence.

We’re not done. But we’re getting closer.

Interested in trying out CodeRabbit’ reviews? Get a 14-day trial!