Use case
Outbound Sales Agents
Your SDR agent runs hundreds of sequences a week. Most of them are fine. One quotes a pricing tier you deprecated six months ago. Another promises a feature that is on the roadmap, not in the product. A third emails a CFO who opted out of contact last quarter. You find out from your AE, then from legal.
Where things go wrong
Deprecated pricing quoted as current
The agent pulls from an outdated context document and quotes a pricing tier that no longer exists. The prospect sees the number, the AE corrects it on the call, and the deal re-enters negotiation from a lower-trust starting point. The agent has no idea it cited a stale number — it produced the output that was faithful to the context it was given, and nothing scored that context for freshness.
Deal delay or collapse; AE credibility damage; no trace of which prompt version generated the incorrect quote.
Roadmap claim sent as a shipped feature
The agent describes a feature as available today. It is six weeks from GA. The prospect builds a procurement case around it. When the feature ships late or ships differently, the contract has a feature expectation baked in that the product cannot meet on the signed timeline.
Contract dispute or renegotiation; sales-to-product relationship strained; legal exposure if the feature claim influenced the purchase decision.
Opted-out prospect contacted
Your suppression list was not included in the agent’s grounding context. The agent emails a contact at a company that submitted a GDPR opt-out request last quarter. The email goes out, and the domain is now at risk.
CAN-SPAM or GDPR violation; burned sending domain; legal review required before outbound resumes.
Personalization hallucination
The agent generates a personalized opener referencing a company milestone — a funding round, a product launch — that it cannot verify from the provided context. The detail is plausible but wrong. The prospect’s first impression is an AI that got them wrong.
Immediate trust loss; the prospect’s read is that the outreach was mass-generated, not researched, regardless of how accurate the rest of the message is.
Eval + control loop
What happens when a rule fires
The response
How TruLayer closes the loop
- Faithfulness
- Hallucination
- Prompt Injection
The failure modes for outbound sales agents are a faithfulness problem. Every claim in an outbound email — pricing, feature availability, company details — should be grounded in a verifiable context document. TruLayer’s faithfulness evaluator checks whether each output span is grounded in what was actually provided as context. When the agent quotes a number or describes a capability, the faithfulness score measures how well that output traces back to the source material. A low faithfulness score on an outbound email means the agent has drifted from its grounding — either because the context was stale, incomplete, or because the model filled in a gap with a confident fabrication. This scores inline as each span arrives, on every sequence, not in a manual spot-check after a deal goes sideways.
The hallucination evaluator catches the complementary failure mode: claims that are not just unfaithful to the provided context but are factually invented — a company milestone that did not happen, a product capability that does not exist, a contact detail that was generated rather than looked up. Both evaluators run on every span. When either fires, the control loop acts before the next send on the same failure path: retry with a more tightly constrained prompt that names the specific fields the agent is permitted to reference; fall back to a model with lower creative latitude; or route the output to a human review queue for sales-ops approval before the sequence continues. The queue holds the message for review — the same failure class does not repeat automatically.
The prompt injection evaluator matters specifically for outbound agents that ingest prospect data from external sources. If a prospect’s LinkedIn bio or company description has been crafted to manipulate the agent’s output — a known class of attack against personalization pipelines — the injected instruction scores inline before the agent acts on it. The per-trace remediation diff shows what changed after the control loop fired: the original eval score, the corrected output, and whether the post-remediation pass met threshold. For a sales-ops team asking "why did the agent say that," the trace is the answer — not a Slack thread.
See it in practice
Instrument your outbound sales agent in two lines.
Wrap your LLM client. Every span from this trace is captured and scored by every built-in evaluator. Eval rules and control-loop actions are configured in the dashboard.
import { TruLayer } from '@trulayer/sdk'
import OpenAI from 'openai'
const tl = new TruLayer({ apiKey: process.env.TRULAYER_API_KEY })
const openai = tl.instrument(new OpenAI())
// Every span from this client is captured, scored by all 25
// built-in evaluators, and surfaced in the outbound project.
// Eval rules + control-loop actions are configured in the dashboard,
// not in your application code.
const response = await openai.chat.completions.create({
model: 'gpt-4o',
messages: [{ role: 'user', content: task }],
})Ship reliable outbound sales agents.
Free tier includes 1M spans / month · No credit card