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A portfolio agent is more trustworthy when its architecture makes unsupported claims hard to publish—not when its prompt merely asks a language model not to hallucinate. Build a curated evidence pipeline, constrain what the agent can retrieve, require claims to cite evidence IDs, and validate those citations in ordinary code. If a claim cannot pass those checks, leave it out.
Why a prompt alone cannot keep a portfolio agent honest
A model can produce a plausible answer even when a portfolio contains no supporting fact. That is a poor trade-off when a visitor is judging a person’s experience: as portfolio-agent builder Owen Adira puts it, “A portfolio agent that invents an employer or a project is worse than no agent at all, because the person reading it is deciding whether to trust me.”
The practical implication is to treat the model as an untrusted component, not the source of truth. Adira’s takeaway is: “The model is the least trusted part of the system; design around that.” The evidence store supplies facts; application code determines which claims can reach the visitor.
Build an evidence base before answering questions
Record where every fact came from
Adira assembled a knowledge graph from existing portfolio data, prepared answers, a CV, and an older personal website. Each fact has an associated evidence node, so a generated claim can be traced to its source rather than treated as free-floating text.
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Assign authority tiers to sources and define how conflicts are resolved. If two sources disagree about employment dates or a project URL, retain both as candidate claims and record which version wins under the authority rules. This makes a correction auditable and avoids silently blending incompatible details.
Remove sensitive data at ingestion
Adira’s implementation removes phone numbers and street addresses before creating evidence. Applying that filter before facts enter the graph is safer than relying on a later prompt to avoid disclosing them: sensitive values are not available to retrieve in the first place.
Keep retrieval narrow and predictable
Use a controlled sequence for each question: first establish that it concerns supported public work, then select a topic and run fixed, pre-written graph reads. Do not let the model invent arbitrary graph queries. Adira’s setup caps work at four graph calls, 24 records, and 12 seconds per question.
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Trusted-domain web search can supply context when needed, but it should not outrank curated portfolio evidence. External context may help explain a technology or term; it cannot establish that a person used that technology unless the portfolio’s evidence supports the claim.
Make read contracts topic-specific
For each supported topic, define the graph reads and the kinds of records they may return. That limits accidental data exposure and makes the answer path easier to test: a question about a project should not trigger unrestricted access to every fact in the graph.
Ask for evidence-linked claims, not unrestricted prose
Have the model return a structured set of short claims, each paired with evidence IDs. In Adira’s implementation, the schema allows up to five claims and up to eight evidence IDs per claim. It has no open-ended field for extra prose, which reduces the opportunity to append an uncited assertion after otherwise grounded claims.
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Then validate each claim in application code before displaying it. The validator should check that:
- Every cited evidence ID was actually supplied to the model for that request.
- At least one cited source is from the curated graph.
- The claim is consistent with the cited evidence and its provenance.
- Any citation URL is copied from the evidence record, not invented by the model.
Validate claims individually rather than rejecting an entire response because one claim fails. Drop an invalid claim while preserving any claims that pass. If none survive, return a clear insufficient-evidence response instead of asking the model to fill the gap with a guess.
Protect visitor privacy and keep operations useful
In Adira’s production setup, visitor questions are not stored. Traces are used only in local development, and operational logs use fixed failure-reason codes. Infrastructure alert emails do not include the visitor’s question. These choices separate debugging information from the personal content a visitor might submit.
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Fixed reason codes can still help operators distinguish failures—such as unsupported topics, invalid citations, or no surviving claims—without copying question text into logs. Decide what the system needs to diagnose before enabling production logging, and avoid retaining visitor content merely because it is convenient.
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Adira’s implementation uses Mastra for workflows, agents, tools, the HTTP server, and local development inspection; Neo4j for the knowledge graph; and a configurable OpenAI-compatible model for structured claims. Railway hosts the Mastra server and Neo4j as two services that communicate over a private network, while the portfolio site calls the agent API.
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Evaluate required facts, forbidden facts, and latency
Create golden questions with two explicit checks: what the answer must include and what it must not include. For example, a question such as “What has Owen built with Angular?” should be checked against both the relevant project evidence and the absence of unsupported projects, employers, or dates. Run the cases offline against a real model; a successful response to one hand-picked prompt is not evidence that the system is reliable.
Adira reports that, after dropping an output tag and validating claims individually instead of validating the whole object at once, his React questions improved from 10 of 12 answered to 12 of 12. In one model setup, full reasoning exceeded a 30-second response deadline; with reasoning disabled, the model followed the evidence only about half the time. With low reasoning effort, Adira reports grounded answers on every golden case in 12 to 20 seconds. These are results from his implementation and test batch, not an independent benchmark or a guarantee for another model, dataset, or deployment.
Use evaluation to make trade-offs visible. If a faster configuration misses required facts or adds forbidden ones, it is not an improvement. If a response exceeds your latency budget, investigate retrieval limits, schema compliance, and model settings while keeping the same grounding checks in place.
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