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An AI agent can give a fluent, plausible answer and still be wrong because the relevant evidence was missing, out of date, never retrieved, or misread. A more capable model cannot fix every break in that chain. To improve an agent, identify whether the failure began with its sources, retrieval, use of evidence, or workflow—and test the change against real examples.
What does “knowledge” mean for an AI agent?
An agent’s answer can draw on several kinds of information: patterns learned during model training, material retrieved at answer time, results returned by tools, and information retained in memory. Which of these it can use depends on how the agent is built and on the interfaces made available to it.
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Retrieval-augmented generation (RAG) is one way to give a model access to relevant external material. A retrieval system searches a collection, selects passages, and supplies them as context for the model’s response. That can make domain-specific information available without relying only on what the model learned during training. But RAG does not certify the source, guarantee that the search found the right passage, or ensure that the model follows the supplied evidence.
Why does an AI agent give wrong answers?
Separate the path to an answer into three questions: was suitable evidence available, did the system retrieve it, and did the model use it correctly? A wrong answer can arise at any of these points, and each calls for a different fix.
1. The source is missing, stale, or unsuitable
First check whether the collection contains evidence that actually answers the question. A passage that uses the right terminology is not automatically authoritative. Review who produced it, when it was published or updated, what it was intended to establish, and whether that source is appropriate for this decision.
For example, a current product policy is usually more relevant to a question about today’s rules than an old help article or an informal discussion. If the answer-bearing material is absent, changing the prompt or increasing the amount of retrieved text will not supply it. Add or update appropriate sources, or have the agent explain that the information is unavailable.
2. Retrieval misses the evidence or adds too much noise
Even a well-maintained collection can produce poor context. The search may fail to return the passage that contains the answer, select a similar but inapplicable passage, or include so much unrelated material that useful evidence is hard to distinguish. OpenAI’s Optimizing LLM Accuracy guidance describes both wrong context and excessive irrelevant context as ways retrieval can undermine an answer.
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Inspect the passages actually passed to the model, not just the search query or the contents of the source library. Check whether the retrieved text contains the answer, whether separate chunks have lost necessary surrounding context, and whether distracting results crowd out the useful ones. Aim for relevant evidence with enough context to interpret it—not simply a larger bundle of text.
3. The model has good evidence but uses it incorrectly
Retrieval can succeed while the final answer still contradicts, overstates, or overlooks the supplied material. Compare the answer with the retrieved passages. Does it preserve the passage’s qualifications? Does it distinguish what the source says from an inference? Does it claim more than the evidence supports?
This is a model-use problem, not necessarily a search problem. Depending on the failure, you may need clearer instructions, a different answer format, or a requirement to identify supporting evidence and state when the evidence is insufficient. The appropriate test is whether the answer follows the evidence, not whether it sounds confident.
How do I make an AI agent more accurate?
Debug from source to answer so that you fix the stage that failed rather than adding complexity by default. Keep a small set of representative questions with expected answers and supporting evidence; use it to check whether a change helps the task that matters.
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- Inspect retrieved context. Record what the model actually received. Verify that the needed passage appears, that its meaning survived chunking, and that irrelevant material is not overwhelming it.
- Check the answer against the evidence. Mark unsupported claims, omitted qualifications, and incorrect inferences. If the evidence was adequate, focus on instructions and model behavior rather than expanding the source library.
- Trace the workflow. Follow the sequence of model calls, tool calls, guardrails, and handoffs. A wrong tool choice or failed handoff can produce a bad answer even when the source material and retrieval are sound.
- Repeat the evaluation after changes. Run the same representative cases after editing sources, prompts, retrieval, or routing. Compare results against task-specific criteria so that a local improvement does not conceal a regression elsewhere.
For implementation teams, tracing and repeatable evaluation serve different purposes. Traces help explain what happened in an individual run; a dataset and repeatable runs help compare behavior across changes. Anthropic’s discussion of evaluation names Braintrust, LangSmith, Langfuse, and Arize Phoenix as examples of tools in this broad area. Choosing among tools depends on needs such as workflow tracing, offline evaluation, production observability, data residency, and integrations; the quality of the tests remains central.
What should an agent evaluation measure?
Define success for the work the agent is supposed to do. A market scan, due-diligence review, and scientific report do not have identical standards for completeness or evidence. A useful evaluation rubric makes the relevant criteria explicit rather than treating every answer as a generic correctness question.
- Groundedness: Are factual claims supported by the available evidence?
- Coverage: Does the answer address the material parts of the request without omitting important findings?
- Correctness: Are conclusions and details right for the task, including its scope and qualifications?
- Source authority: Are the cited or relied-on sources appropriate for the claim being made?
- Uncertainty: Does the agent acknowledge when evidence is incomplete instead of filling the gap with a plausible guess?
Evaluation can accidentally reward the wrong behavior. OpenAI’s paper on grading notes that if abstaining is penalized, a model that is unsure may be pushed toward guessing. Decide how an agent should respond when evidence is insufficient, and score that behavior accordingly.
For tool-using agents, judge progress against evidence from the environment, such as tool results or code execution, rather than treating the agent’s narration as proof that a task succeeded. A claim that an action worked is not itself the result of that action.
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Choose the intervention based on the failure. These approaches address different needs, and none substitutes for checking whether the system works on the intended task.
| Approach | Best suited to | What it does not fix by itself | What to evaluate |
|---|---|---|---|
| Prompt changes | Unclear instructions, response structure, or rules for handling evidence and uncertainty | Missing or outdated source material, a retriever that misses the answer, or a broken tool workflow | Whether the agent follows instructions and produces the required answer from the evidence provided |
| RAG | Giving the model access to relevant domain information at answer time | Unreliable sources, poor retrieval, noisy context, or incorrect use of retrieved evidence | Whether appropriate passages are retrieved and whether the answer follows them |
| Fine-tuning | Teaching task behavior or response patterns that need to be learned consistently | Automatically supplying current facts or guaranteeing that a particular source is retrieved | Whether the target behavior improves without harming other important cases |
OpenAI distinguishes RAG’s role in providing in-context or domain knowledge from fine-tuning’s role in teaching task behavior. Its guidance recommends evaluating the target and getting adequate performance from simpler methods before adding more complex machinery. A prompt change is not a replacement for missing evidence; fine-tuning is not a live source of current facts; and adding RAG is not proof that the answer is grounded.
What can benchmarks tell you about agent quality?
A benchmark measures performance on a defined set of tasks, not every kind of work a reader might ask an agent to do. OpenAI describes BrowseComp, published in 2025, as a benchmark of 1,266 challenging browsing questions with short, verifiable answers. Its authors note that this short-answer setup may not correlate with open-ended real-user work.
Use benchmark results as evidence about the benchmark’s task. For your own agent, also test representative requests, expected evidence, tool outcomes, and the cost of an unsupported answer. A strong score on one test is not a universal certificate of quality.
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Change one part of the path at a time where practical, and preserve the cases that exposed the original failure. If the source changed, verify that the new material is current and authoritative; if retrieval changed, inspect the returned passages; if instructions changed, check that answers now handle evidence and uncertainty as intended. Then rerun the same cases and look for regressions in other representative requests.
A reliable agent is not merely one that can produce a fluent answer. It is one whose sources, retrieval, tools, and evidence use suit the task—and whose evaluation rewards a well-supported answer or an honest admission that the evidence is not enough.
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