To build an AI web research agent that people can trust, start with evidence rather than search summaries. The agent should define the question, identify authoritative sources, retrieve current pages under known conditions, extract structured facts, separate reporting from opinion, and attach citations and timestamps to every material claim. GPT-5.6 can plan difficult research, reformulate searches, reconcile sources, and write the final analysis. BrowserAct provides the live browser execution a

The finished research-agent architecture

Research question and data contract
  → GPT-5.6 creates source plan and claim schema
  → BrowserAct opens current primary sources
  → Evidence collector stores text, state, URL, time, and screenshot
  → Extractor creates structured claim records
  → Verifier checks authority, freshness, support, and conflicts
  → GPT-5.6 synthesizes only verified records
  → Citation renderer produces linked output
  → Scheduler refreshes volatile fields and flags changes

The model is not the database. Search is not the evidence store. The browser run produces observations; the claim store preserves them; the model reasons over that controlled corpus.

Step 1: Define a testable research question

“Research GPT-5.6” is too broad. A better contract is:

As of July 13, 2026, compare GPT-5.6 Sol, Terra, and Luna for browser-agent workloads. Return official pricing, context, reasoning settings, published browsing/computer-use results, release availability, source URLs, retrieval times, and any benchmark comparability warnings.

The contract defines:

• Entity: GPT-5.6 family.
• Audience: teams building browser agents.
• Time boundary: as of a specified date.
• Fields: pricing, technical limits, benchmarks, and availability.
• Source policy: official OpenAI sources for specifications; reputable direct reporting for context.
• Evidence: URL, retrieval time, relevant visible text or table, and screenshot for key claims.
• Output: structured comparison plus a narrative conclusion.

This prevents the agent from collecting interesting but irrelevant material.

Pro Tip: Add explicit exclusions. For example: “Do not treat search snippets, unsourced social posts, or different OSWorld versions as direct benchmark evidence.” Negative rules eliminate predictable research errors.

Step 2: Create a source hierarchy

Rank source types before searching:

When sources conflict, prefer the source closest to the fact. The API model page beats a news article for current token price. A court filing beats commentary about the filing. A company's authenticated dashboard beats a cached search result about the account.

Step 3: Define the claim and evidence schema

Store one material claim per record:

{
  "claim_id": "gpt56-sol-input-price",
  "subject": "GPT-5.6 Sol",
  "predicate": "input_price_per_million_tokens",
  "value": 5,
  "unit": "USD",
  "claim_type": "fact",
  "source": {
    "publisher": "OpenAI",
    "url": "https://developers.openai.com/api/docs/models/gpt-5.6-sol",
    "tier": 1,
    "retrieved_at": "2026-07-13T08:00:00Z"
  },
  "page_state": {
    "region": "US",
    "section": "Pricing"
  },
  "evidence": {
    "visible_text": "Input $5.00",
    "screenshot": "run://gpt56/sol-pricing.png"
  },
  "verification": "verified"
}

This format lets the verifier reject a claim that has a value but no provenance. It also lets the scheduler refresh only volatile predicates such as price or availability.

Useful claim_type values are:

• fact: directly supported by the source.
• source_claim: a vendor, customer, or reporter asserts it.
• inference: the research agent derives it from verified facts.
• opinion: a source expresses judgment or preference.

Step 4: Give BrowserAct a bounded collection task

Install the BrowserAct skill for a local agent, or call BrowserAct through API/MCP in an application. The browser task should request observable outputs, not a vague summary.

Open the official GPT-5.6 Sol model page.
Use the public US page state.
Extract model ID, context window, max output, knowledge cutoff,
input price, cached input price, and output price.
Return the source URL, retrieval time, visible evidence for each field,
and one screenshot of the pricing/specification section.
Do not infer missing values.

BrowserAct can return clean indexed page structure so the agent can find relevant controls and text without consuming the raw DOM. It can navigate dynamic pages, select filters, reuse a signed-in Chrome session when the authorized source requires it, and preserve artifacts.

For each source, save a collection status:

• verified: required evidence captured.
• partial: some fields missing or ambiguous.
• blocked: access or permission prevented retrieval.
• stale: source does not meet the freshness rule.
• rejected: source fails the hierarchy or relevance policy.

Step 5: Extract facts without losing context

Extraction should preserve the qualifiers that make a value true.

“$5” is not enough. The complete fact is “GPT-5.6 Sol standard API input is $5 per one million tokens, before long-context pricing adjustments, retrieved from the official model page on July 13, 2026.”

Capture:

• subject and field;
• value, unit, and interval;
• applicable plan, region, account, or date;
• footnotes and exceptions;
• source authority and publication date;
• retrieval time;
• exact visible supporting fragment;
• screenshot or downloadable artifact for material claims.

Tables require special care. Confirm headers remain attached to values. A model price can be assigned to the wrong column when a responsive table collapses or copied text loses structure.

Pro Tip: Ask the browser collector to return headers and row labels with every table value. The verifier should reject orphan numbers even when they look plausible.

Step 6: Separate facts, claims, and opinions

Research agents often turn vendor language into neutral fact. Keep the layers visible.

Do not resolve a conflict by averaging incompatible facts. First check date, region, model version, benchmark version, harness, and reasoning setting.

Step 7: Verify claims before synthesis

Run a deterministic gate over the claim store:

1. Required fields exist.
2. Source tier is allowed for the claim type.
3. Retrieval time meets the freshness rule.
4. Visible evidence supports the exact predicate and value.
5. Page state contains required region, account, or filter context.
6. No unresolved higher-authority conflict exists.
7. Benchmark comparisons use the same evaluation version and setup.
8. Sensitive data is permitted in the output.

Then ask GPT-5.6 to challenge the surviving corpus: Which conclusion is weak? Which claim has only first-party support? Which values changed between sources? What evidence would falsify the conclusion?

The critic should not browse independently without recording new evidence. Otherwise the reviewed corpus and the final text diverge.

Step 8: Generate citations that support exact claims

Each linked citation should point to the page that supports the nearby sentence. Avoid a generic source list with no claim mapping.

For web output, preserve:

• descriptive source title;
• direct URL, not search result URL;
• publisher;
• publication or update date when available;
• retrieval date for volatile data;
• evidence ID linking back to the claim store.

When a page is likely to change, write “retrieved July 13, 2026” or display an as_of field. Screenshots help audit the historical observation but should not replace the clickable source when the page remains available.

Step 9: Produce the analysis from verified records

Give the writer only records with verified status plus explicitly permitted opinions. A useful synthesis prompt is:

Write the comparison using only the supplied claim records.
Do not introduce facts from memory.
Attribute vendor claims and reviewer opinions.
If two records conflict, describe the conflict and its likely cause.
Place the supporting source link next to each material claim.
End with unresolved questions and the dataset retrieval time.

This makes the model's role clear: organize and interpret evidence, not silently fill gaps.

Step 10: Refresh the research by volatility

Not every field needs the same schedule:

BrowserAct workflows can schedule retrieval and return the same schema each time. The agent then analyzes the delta instead of repeating the entire research project.

Pro Tip: Refresh conclusions, not just fields. A price change may invalidate a routing recommendation even if every other paragraph remains accurate.

Worked example: researching the GPT-5.6 launch

A strong GPT-5.6 research run would collect:

• the OpenAI launch page for family positioning and published evaluations;
• official API model pages for identifiers, context, cutoff, reasoning levels, and price;
• direct reporting for release timing and product availability;
• independent evaluation or review for outside methodology and observed behavior;
• BrowserAct pages and docs for current execution-layer capabilities when discussing integration.

The verifier would flag at least three traps:

1. GPT-5.5 OSWorld-Verified and GPT-5.6 OSWorld 2.0 are not direct raw-score comparisons.
2. Claude Mythos 5 and Claude Fable 5 share an underlying model but do not have identical deployed safeguards.
3. Token price is not total cost per completed browser task.

Those warnings are exactly why a research agent needs claim-level evidence rather than a fluent summary of search results.

Measure research quality

Track:

• verified material claims divided by total material claims;
• primary-source coverage;
• citation-to-claim support rate;
• freshness compliance;
• unresolved conflict rate;
• unsupported inference rate;
• cost and time per verified claim;
• change-detection precision;
• reviewer corrections after publication.

A long report with 100 links can score poorly if the links do not support its conclusions. Optimize for evidence density and decision value.

Build the agent around evidence, not confidence

You can build an AI web research agent that moves faster without giving up traceability. Let GPT-5.6 plan, compare, challenge, and synthesize. Let BrowserAct retrieve the current primary pages, operate the required browser state, and return structured evidence.

The resulting system does not merely answer a question. It shows what was observed, where, when, under which conditions, and how the conclusion follows. That is the difference between automated summarization and reliable research.

Use Live Web Data for AI Agents for the data contract, and AI Agent Browser Execution Layer for the runtime architecture.

Build a research agent grounded in real web sources →

Sources

• BrowserAct documentation
• BrowserAct Skill
• BrowserAct skills on GitHub
• OpenAI — GPT-5.6
• OpenAI API — GPT-5.6 Sol
• TechCrunch — GPT-5.6 release coverage
• The Verge — GPT-5.6 and ChatGPT Work

Frequently asked questions

What does an AI web research agent do?

It defines a research plan, retrieves current sources, extracts claim-level evidence, verifies freshness and authority, resolves conflicts, and produces a cited analysis.

Why should a research agent prioritize primary sources?

Primary sources are closest to specifications, filings, datasets, policies, and direct statements. Secondary sources remain valuable for independent context and criticism.

Can GPT-5.6 research the web by itself?

GPT-5.6 can plan and interpret research, but the application still needs tools or a browser runtime to retrieve current sources, preserve page state, and capture evidence.

How does BrowserAct help a research agent?

BrowserAct opens live pages under the required browser identity, returns clean page observations, executes interactions, extracts structured facts, and captures URLs, timestamps, screenshots, and artifacts.

How should the agent handle conflicting sources?

Compare authority, date, region, version, methodology, and page state. Preserve the conflict when it cannot be resolved, and do not average incompatible claims.

How often should an AI research agent refresh its results?

Refresh by field volatility. Launch availability may need hourly checks, pricing daily or weekly checks, and stable architecture content only after relevant product changes.