When your agent behaves unexpectedly,
you're one step away
from a complete log reconstruction exercise.
Your agent did something unexpected. Your manager, customer, or regulator is asking why. You have 10 minutes to explain what happened. WhyAgent makes that a 30-second query.
Free during beta. No credit card required.
See how it works
The Accountability Gap
When agents act in your name, can you explain why?
When an AI agent makes an unexpected decision, you're left reconstructing what happened through raw execution logs, model traces, and tool outputs. It's a forensic exercise that takes hours when you need answers in minutes—whether you're presenting to the VP in 2 hours, explaining bias to engineering teams, or documenting decisions for regulatory compliance.
What your current tools show
Execution traces answer "what happened" but can't tell you why your agent made those choices.
[2026-02-19 09:14:22] Support agent received ticket #47291 [2026-02-19 09:14:23] Classification: billing_dispute, priority: high [2026-02-19 09:14:24] Knowledge base lookup: 3 articles retrieved [2026-02-19 09:14:25] Confidence score: 0.62 [2026-02-19 09:14:26] Action: escalate_to_human [2026-02-19 09:14:26] Human agent: not_available [2026-02-19 09:14:27] Customer response: "This is unacceptable" [2026-02-19 15:23:18] Code review agent processed PR #1847 [2026-02-19 15:23:19] Files analyzed: 12, changes: 847 lines [2026-02-19 15:23:21] Action: reject [2026-02-19 15:23:21] Reason: "Code quality issues detected"
❓ Why did it escalate when no human was available?
❓ Why is the data team getting rejected 3x more?
❓ How do I explain this to stakeholders?
What WhyAgent shows
Decision audit trails answer "why it decided to do that" with actionable insights for optimization.
Query: "Why did agent escalate ticket #47291 instead of resolving?" Decision: escalate_to_human Context: Customer = enterprise_tier, issue = billing_dispute, amount = $12,400 Alternatives Considered: • auto_resolve: Confidence too low (0.62 < 0.75 threshold) • knowledge_base: No exact match for enterprise billing policy • escalate_to_human: Policy requires human review for enterprise + billing Decisive Factor: Enterprise customer SLA + billing dispute keywords Model Reasoning: "High-value customer billing disputes require immediate human review per policy P-47" Query: "Why did agent reject PR #1847 from data-team?" Decision: reject_pull_request Context: Team = data_team, files = 12, complexity_score = 8.4/10 Alternatives Considered: • approve: Code functional but exceeds complexity threshold • request_changes: Specific issues identified in 3 files • reject: Multiple violations of style guide + complexity Decisive Factor: data_team has 3x higher reject rate due to different linting config Model Reasoning: "Inconsistent code style standards between teams triggering false positives"
✓ Clear reasoning for every choice
✓ Cost impact of decisions
✓ Optimization paths to fix waste
Current observability tools (LangSmith, Arize, Braintrust) track inputs, outputs, latency, and token counts. That's essential.
WhyAgent shows why your agents made those specific choices. That's what's missing.
Instrument decisions,
not just executions
WhyAgent adds decision observability to your existing stack. One SDK call captures every choice your agent makes with full context.
// Agent decision point
const model = await modelSelector.choose({
task: "classify_sentiment",
inputLength: 67,
context: "user_feedback",
platform: "bedrock",
priority: "balanced"
})
// Decision: claude-3.5-sonnet selectedQuery Like Data
Find decision patterns with SQL-like queries across all your agent decisions
Optimize Precisely
Identify exact decision points causing inefficiency, not just symptoms
Learn & Improve
Understand your agent's reasoning patterns and systematically improve them
Ready to Never Be Caught
Off-Guard Again?
We're working with 5 design partners to build WhyAgent around real incident response needs. If you're running AI agents in production and have ever struggled to explain their behavior to stakeholders, let's talk.
Request Early Access
Tell us about your AI agent setup. We'll prioritize teams who have faced "explain why our AI did that" moments with stakeholders.
We'll review your application and reach out within 48 hours.
What you'll get in beta
Design partner benefits
Not ready to apply yet?
Follow our progress at updates@whyagent.dev
"The teams that need this
already know they need it"
When agents behave unexpectedly, these teams face the same challenge: explaining AI decisions to stakeholders who don't care about logs.
These scenarios happen every day in production AI systems.
The pain isn't cost—it's being unable to explain your AI's behavior when someone important asks.