The trust
boundary
for enterprise
agents.
Fydel gives teams policy, audit, and evidence controls for agents that retrieve, synthesize, and act on information, starting with evidence admission, review decisions, and lightweight source signals.
Built by former Meta Trust & Safety engineers who designed integrity systems at scale.
Raw evidence
Fydel boundary
read, say, act
Evidence strength
weak or mixed
Source signals
origin checked
Answer confidence
too certain
Policy action
next step
Needs human check
Tone it down
Safe to use
Ready to share
Adjusted decision
Uses checked evidence
Agent output
Output adjusted before use
Audit trail
1 review · 1 caveat · 2 proceed
Agents are entering production before trust controls are ready
AI agents are moving from pilot to production. Security and governance teams are allocating budget. But most controls still focus on prompts, model behavior, or runtime posture, while the boundary between external evidence, synthesized output, and downstream action remains underbuilt.
One deployment failure pattern
INGEST
Agent reads
SYNTHESIZE
Agent summarizes
UNCERTAIN
Signals collapse
ACTION
Agent proceeds
INGEST
Agent reads
SYNTHESIZE
Agent summarizes
UNCERTAIN
Signals collapse
ACTION
Agent proceeds
Evidence enters
An agent pulls external content into a compliance, research, or monitoring workflow.
Context is synthesized
The agent turns mixed sources into a brief, score, recommendation, or next-step action.
Uncertainty disappears
Weak evidence, missing provenance, source disagreement, or overconfident language gets flattened.
Accountability breaks
Teams cannot easily answer what the agent trusted, why it proceeded, or what should have been reviewed.
This is just one pattern. Unverifiable evidence, overconfident synthesis, unclear accountability, and review bottlenecks all exploit the same missing boundary.
Why now
Three converging forces make agent trust boundaries urgent.
Built for high-trust agent workflows
The first teams that feel this pain are the ones shipping agents into workflows where evidence quality, synthesis, and action policy create real operational risk.
Primary design-partner workflow
Compliance and vendor-risk workflows
Internal agents that retrieve external sources to support diligence, procurement, and monitoring decisions.
Why it matters: Weak evidence and unclear policy turn into bad decisions, audit gaps, and extra review.
Research and market-monitoring agents
Teams using agents to summarize news, market chatter, public filings, and third-party analysis.
Why it matters: A grounded but overconfident synthesis can distort the brief your team acts on.
Trust, safety, and intelligence systems
Workflows that need to separate authentic signals from manipulation while preserving a reviewable evidence trail.
Why it matters: The hard part is not reading more content. It is deciding what agents can rely on and what needs review.
Built from the adversary's playbook
Trust & Safety built this playbook for platforms. Now enterprise agents need it.
Most teams can wire an agent to the web. Very few can build the adversarial datasets, signal models, policy logic, and audit trail needed to decide what agents can read, cite, say, or do from external evidence. Fydel starts with evidence admission, policy decisions, and source signals, then deepens source trust as the trust graph grows.
- Adversarial datasets: trained on the manipulation patterns behind coordinated campaigns at Big Tech, not just clean-web benchmarks
- Signal models: built to turn source type, provenance, authenticity cues, freshness, and policy thresholds into usable admission decisions
- Policy logic: encodes the decision frameworks used to act on ambiguous signals, review thresholds, and audit needs in high-volume pipelines
Design partner fit
Best for teams already shipping or piloting agents that retrieve third-party content, synthesize findings, or trigger reviews in high-trust workflows. We're onboarding a small cohort to shape the SDK and policy API together.
Where existing tools stop short
Prompt-injection scanners catch malicious instructions. Source-rating datasets score known publishers and false-claim narratives. RAG evals check whether individual claims are grounded. Fydel's boundary adds the missing workflow layer: evidence admission, policy decisions, audit events, and epistemic checks for whether an agent's answer fairly represents the evidence it used.
Evidence admission
Decides whether external evidence can enter an agent workflow, needs review, or should be held out.
Epistemic output checks
Checks synthesis faithfulness, confidence calibration, and disagreement handling before an answer shapes decisions.
Lightweight source signals
Starts with source type, provenance, domain authenticity, freshness, known lists, and workflow-specific policy thresholds.
Agent-native policy and audit
Returns taxonomy-tagged reasons, policy decisions, and audit events so agents can proceed, caveat, review, or block programmatically.
Integrate a trust boundary into your agent runtime.
Fydel's SDK sits at the points where agents admit evidence, synthesize answers, and take action. It calls Fydel's policy API, enforces the returned decision, and records an audit trail for what the agent trusted, changed, reviewed, or blocked.
import { boundary } from "@fydel/agent"
await boundary.beforeSynthesis({
workflow: "market_monitoring",
purpose: "prepare analyst brief",
policyId: "pol_market_intel",
agentId: "agt_market_monitor",
taskId: "task_fed_watch_0429",
evidence: [{
id: "ev_reuters_clone",
type: "external_evidence",
url: "https://reuters-breaking.co
/markets/fed-rate-pause",
content: "Federal Reserve signals
extended rate pause amid..."
}],
draftOutput: "The Fed is clearly preparing..."
}){
"request_id": "bnd_8f2a1c4e",
"checkpoint": "before_synthesis",
"confidence": "medium",
"decision": "review_required",
"evidence_actions": [
{
"evidence_id": "ev_reuters_clone",
"action": "hold_for_review"
}
],
"output_action": "require_caveat",
"next_step": "pause_for_review",
"policy_result": {
"policy_id": "pol_market_intel",
"matched_rule": "unknown_publisher_review"
},
"source_signals": {
"mode": "v0_policy_signal",
"source_type": "unverified_publisher",
"provenance": "missing",
"domain_authenticity": "suspicious",
"list_match": "none"
},
"epistemic_checks": {
"synthesis_faithfulness": "needs_context",
"confidence_calibration": "overstated",
"disagreement_handling": "missing"
},
"audit_event_id": "aud_3b91d7e2",
"reasons": [
{
"dimension": "source_authenticity",
"detail": "Source signals are provisional:
no allow-list match and weak
provenance for this workflow"
},
{
"dimension": "policy_audit",
"detail": "Workflow policy requires review
before this evidence can shape
agent output or action"
}
],
"assessed_at": "2026-03-16T14:32:07Z",
"latency_ms": 184
}
How it works
Add a trust boundary to your agent without rebuilding trust and safety infrastructure in-house. The first control point governs whether evidence should enter a workflow at all, using policy, provenance, audit, and lightweight source signals. The same boundary can later deepen into source trust across contexts and answer trust with epistemic checks that go beyond claim-level grounding.
External Evidence
article, report, filing, or profile
Fydel Signal
Source type, provenance, policy cues
Policy Check
Workflow rule requires review
Audit Event
Evidence, rule, and decision logged
Next Step
REVIEW REQUIRED
Step 1
Agent gathers evidence
Your AI agent crawls a webpage, reads a report, summarizes filings, or receives third-party content.
Step 2
Fydel checks boundary context
Before proceeding, the Fydel SDK sends evidence, source context, workflow purpose, and policy to the boundary.
Step 3
Policy returns a decision
The boundary returns a decision with confidence, taxonomy-tagged reasons, and required review state.
Step 4
Audit trail records it
Your system records what evidence entered, what policy matched, and whether it proceeded, caveated, reviewed, or blocked.
Answer-trust roadmap
Grounded answers can still mislead.
Factual consistency asks whether each claim has a source. The next layer asks whether the agent represented the evidence honestly: enough context, appropriate certainty, suitable sources, and visible disagreement when the evidence is mixed.
Factual grounding
Checks whether each individual claim is supported by identified evidence.
Synthesis faithfulness
Flags cherry-picking, selective omission, and summaries that misrepresent the source set.
Confidence calibration
Compares confident or hedged language against the actual strength of the evidence.
Source authority
Asks whether cited sources are appropriate and representative for the claim domain.
Disagreement handling
Surfaces genuine disagreement instead of compressing conflicting evidence into false consensus.
Become a design partner
We're selecting a small cohort of teams to shape Fydel and the agent trust boundary. Tell us where evidence enters your workflow and we'll schedule a call within 48 hours.
Book a design partner call
Skip the form. Walk us through your agent workflow and we'll show you where a trust boundary could fit.
Schedule a callGet an agent trust boundary review
Share a sample workflow and we'll map where evidence admission, source signals, policy decisions, and audit events should sit before you integrate.
Request a reviewPrefer a direct conversation? Email the founder.