A Reflection Agent is an AI system that reviews and improves its own work before delivering the final output. Instead of generating an answer and immediately presenting it, a reflection agent acts like its own quality reviewer, checking for errors, inconsistencies, and areas where it could do better.
The concept comes from how humans think. Psychologists describe "System 1" thinking (fast, instinctive reactions) and "System 2" thinking (slower, more deliberate analysis).
Traditional AI operates mostly in System 1 mode, giving quick answers without pausing to check its work. Reflection agents add System 2 capabilities, making them more thoughtful and accurate.
Research shows this approach dramatically improves performance. In one study, adding reflection to a coding task improved accuracy from 80% to 91%, all without upgrading the underlying AI model.
For businesses, this means higher-quality outputs with fewer errors, whether you're processing invoices, drafting customer responses, or analysing contracts. The tradeoff is that reflection takes more time and computational resources, but for tasks where accuracy matters more than speed, the investment pays off.
How is a reflection agent different from a regular AI agent?
A regular AI agent generates output directly, like someone answering a question off the top of their head. A reflection agent adds quality control steps. After generating an initial answer, it reviews what it created, identifies weaknesses, and produces an improved version.
For example, if you ask a regular AI agent to write code for calculating shipping costs, it might produce code with syntax errors or edge cases it didn't consider. A reflection agent would run the code internally, spot the errors, fix them, test again, and only deliver the final code once it runs correctly. This self-review process happens automatically before you see any results.
How does this actually work in practice for business tasks?
Let's say you're using an AI agent to review purchase orders for compliance issues. A reflection agent would first analyze the purchase order and draft findings about potential compliance problems.
Then, instead of sending that draft to you immediately, it would critique its own analysis by asking questions like "Did I check all the relevant regulations?" or "Are there edge cases I missed?"
Based on this self-critique, it revises its findings. This might happen two or three times. You only see the final, refined compliance report. Zamp applies this pattern to invoice processing, where agents first extract data from invoices, then verify their own extractions against purchase orders, flag inconsistencies for review, and only mark items as "approved" when all checks pass.
Does reflection make AI agents slower?
Yes, reflection requires multiple processing cycles instead of one, so it takes longer than a single-pass approach. However, the time difference is often measured in seconds, not minutes. For a code generation task, a regular agent might respond in 5 seconds while a reflection agent takes 15 seconds.
For most business processes, this tradeoff makes sense. If you're processing 1,000 invoices and reflection reduces errors from 5% to 0.5%, you save significant manual review time downstream. The extra seconds upfront prevent hours of error correction later.
When should a business use reflection agents versus regular agents?
Use reflection agents when accuracy and quality matter more than raw speed. This includes financial processes (invoice validation, expense report review), contract analysis, code generation, compliance checks, and complex decision-making.
Don't use reflection for simple tasks where mistakes aren't costly, like categorizing customer inquiries or basic data entry.
For example, an AI agent categorizing support tickets into "billing," "technical," or "general" doesn't need reflection because misclassification is low-risk and easy to fix. But an agent approving wire transfers should use reflection to catch errors before money moves.
What about errors the reflection agent might not catch?
Reflection agents significantly reduce errors but aren't perfect. They can still miss edge cases or make incorrect assumptions during the review process. This is why reflection works best combined with human oversight for high-stakes decisions.
Zamp addresses this by implementing multiple safety layers beyond reflection.
Activity logs record every decision the agent makes, so you can audit the entire process. When agents encounter situations they're uncertain about, they flag items with a "Needs Attention" status rather than guessing.
You can configure approval checkpoints at any step, requiring human sign-off before actions finalize. The Knowledge Base lets you define specific rules the agent should follow, and the agent's reflection process explicitly checks against those rules. This layered approach catches errors that pure reflection might miss.
Can reflection agents integrate with our existing business systems?
Yes, reflection agents work as part of your existing workflows. They connect to ERPs, procurement platforms, email, Slack, databases, and other systems through APIs. The reflection process happens internally; you don't need to change your systems to accommodate it.
Zamp's agents integrate with common business tools and run reflection steps automatically within your processes.
For example, when an invoice arrives via email, the agent extracts data, cross-references your ERP system, reflects on potential discrepancies, and either auto-approves or flags for review. All of this happens within your existing email and ERP infrastructure. From your team's perspective, they simply see higher-quality results with fewer errors requiring manual intervention.
What's the learning curve for setting up reflection agents?
The complexity depends on your use case. For straightforward applications like document review or data extraction, you define the task and quality criteria upfront, then let the agent handle reflection automatically.
You don't program the reflection logic yourself. For complex workflows, you might need to customize what the agent checks during reflection.
Zamp simplifies this through the Knowledge Base, where you define quality standards and business rules in plain language, not code. For example, you might write "Invoices over $10,000 require VP approval" or "Flag any price changes exceeding 10% from the PO."
The agent incorporates these rules into its reflection process, checking each item against your criteria before finalizing. Most teams get basic reflection workflows running within days and refine them over time as they learn what quality checks matter most for their specific processes.