DEV Community: Priyam

Why Voice Agent Testing Setup Is Slower Than the Test (And How to Fix It)

Priyam — Fri, 30 Jan 2026 19:24:02 +0000

Voice agent testing often starts with friction:

Provider configuration
API field mapping
Integration work

All before you see a single result.

This is unnecessary.

You can now test voice agents hosted anywhere by simply adding a phone number.
No provider-specific fields. No custom wiring.

It works across Vapi, Retell, and custom voice stacks.

The goal is simple: make testing lighter than building.

Mastering AI Agent Evaluation (2026): Why Simulation Is the Missing Layer

Priyam — Thu, 22 Jan 2026 20:10:32 +0000

AI agent evaluation stacks are reactive.
They measure failures after users experience them.

The 2026 Edition of Mastering AI Agent Evaluation focuses on closing that gap with two new chapters.

Chapter 6: Simulation Environments for Agentic Systems
How to treat simulation as a first-class eval primitive:

Generate realistic scenarios
Test full agent trajectories
Design personas for coverage, not demos

Chapter 7: AI Agent Evaluation in Practice
Concrete, end-to-end workflows for evaluating:

Chat agents (drift, context erosion)
Voice agents (audio streams, interruptions, timing failures)

Includes code you can run.

If you’re an AI PM or engineer building agents for real users and real stakes, this guide is designed for you.

📥 Download Here -> https://shorturl.at/HRemM

New Launch- AI debugging and fixing other AI Agents

Priyam — Wed, 21 Jan 2026 18:38:54 +0000

Hi friends,

We launched Fix My Agent **on **Product Hunt today - quick ask for support!

Built for voice AI/chat agent builders, it’s not just another debugging tool. It diagnoses AI agent failures, auto-implements fixes, and validates the improvement, so you ship what actually works.
Full loop: Diagnose → Fix → Validate → Ship. Automatically.

Your upvote would really help: https://shorturl.at/Snhxj

Thanks!

Why “Just Try Another Prompt” Is Not an Experiment Strategy

Priyam — Mon, 19 Jan 2026 17:38:27 +0000

AI teams say this all the time:

“Let’s try a different prompt or model.”

But AI experimentation isn’t UI A/B testing.

Key differences:

Changes affect meaning, not layout
Evaluation requires reasoning, not CTR
You must test offline before users see results

Prompts × models × parameters create combinatorial chaos

A usable AI experiment pipeline needs:

Prompt versioning with side-by-side evaluation
Model comparisons on the same task
Parameter sweeps that aren’t random
Multi-axis comparison (quality, cost, latency)

A practical workflow:
Step 1: Build or generate a test set
Step 2: Define variants
Step 3: Run evaluations automatically
Step 4: Compare results clearly
Step 5: Deploy with confidence

If every experiment is a manual effort, teams experiment less.
Infrastructure doesn’t slow you down. It’s what enables speed.

How many meaningful AI experiments did your team run last month?

Ready-to-use Personas [Mandatory if you're building agents]

Priyam — Sat, 17 Jan 2026 08:10:07 +0000

Voice and chat agents rarely fail because of ASR noise or model quality.
They fail because production users don’t behave like clean test cases.

Real users:

Interrupt
Get emotional
Switch goals mid-conversation
Ask unclear or contradictory questions

We heard this repeatedly from 150+ AI product managers and engineers:
teams validate flows, but don’t validate who they’re talking to.

That’s why we created a Notion Persona Kit focused on support agents:

10 realistic personas across banking, telecom, ecommerce, insurance, and travel

Built for both voice and chat agents

Designed to expose edge cases early, not after deployment

Get Your Copy Today -> https://forms.gle/Hy4fGHACc616Mo7j7

Why Single-Agent AI Systems Are Being Replaced by Agent Teams

Priyam — Thu, 08 Jan 2026 02:07:41 +0000

Most AI agents today are still designed like monoliths: one prompt, one model, one response.

That works for Q&A.
It fails for anything that looks like real work.

Tasks like competitive research, synthesis across sources, and self-verification expose the limits of solo agents very quickly.

What’s happening now mirrors classic software evolution:

Monolithic apps → distributed services
Single prompts → coordinated agent teams

We built a production research workflow using CrewAI to explore this shift end-to-end.

In our latest newsletter, we cover:

The architectural reasons solo agents break down
What production multi-agent systems actually look like
How teams evaluate and catch failures before users do

Multi-agent systems aren’t an experiment anymore.
They’re how complex AI work gets done reliably.

📖 Read the full breakdown here -> https://shorturl.at/5PmDc

Why Image Hallucination Is More Dangerous Than Text Hallucination

Priyam — Tue, 06 Jan 2026 03:15:52 +0000

We’ve spent a lot of time talking about text hallucinations.
But image hallucination is a very different and often more dangerous problem.

In vision-language systems, hallucination isn’t about plausible lies.
It’s about inventing visual reality.

Examples:

Describing people who aren’t there
Assigning attributes that don’t exist
Inferring actions that never happened

As these models are deployed for:

E-commerce product listings
Accessibility captions
Document extraction
Medical imaging workflows

…the cost of hallucination changes from “wrong answer” to “real-world consequence.”

The issue is that most evaluation pipelines are still text-first.
They score fluency, relevance, or similarity but never verify whether the image actually supports the description.

Image hallucination requires multimodal evaluation:

Compare generated text against visual evidence
Reason about object presence, attributes, and relationships
Detect contradictions between image and output

This isn’t a niche problem.
It’s an emerging reliability gap as vision models move into production.

Curious how others are approaching hallucination detection for image-based systems.

Free AMA for Agent Builders: Debugging, Evals, and Reliability

Priyam — Tue, 16 Dec 2025 20:00:21 +0000

What's better than fixing your agent?

Fixing it live. With people who've been exactly where you are. While everyone's watching it click into place.

That's what's been happening every Wednesday at 9:30 AM PT.

Week 3 tomorrow. Your bugs. Our engineering team. Those moments that make you go "FINALLY."

Why Transcripts Aren’t Enough for Debugging Voice AI (And What to Use Instead)

Priyam — Mon, 15 Dec 2025 17:29:06 +0000

Voice AI teams still rely on transcripts for debugging.
But a transcript only shows the surface of the system. The real debugging context lives deeper.

A voice call is a pipeline:
Audio → ASR → LLM → Tools → TTS → Audio Output

A delay in ASR affects the LLM.
A stalled tool call affects timing.
A weak TTS response breaks user experience.

Transcripts don’t show latency patterns, tool behavior, blocked branches, or reasoning failures.

This is why we built Voice Observability in SIMULATE.

Instead of logging text, we trace the entire execution:

Audio in/out with timestamps
ASR events and confidence shifts
LLM reasoning paths and tool calls
TTS generation + round-trip latency
Behavior regressions across runs

You also get a single, continuous session view, no stitching logs from multiple systems.

And it works across stacks like Vapi, Retell, LiveKit, Pipecat, plus custom voice pipelines.

Voice agents are finally hitting production scale.
Relying on transcripts is like debugging a distributed system with print statements.

Full observability is the engineering baseline.

🔗 Learn More -> https://shorturl.at/Jfu6S

[Challenge] Create Voice Agents in Minutes

Priyam — Tue, 09 Dec 2025 17:21:16 +0000

“Agent configs” start life as perfectly manicured.
6 weeks later: final_v7_new_latest_backup(2) 🫠

New ideas → new agents → scattered tests → no one knows which config actually worked.

Agent Configuration in Future AGI fixes the config chaos at the source:
• 3-step workflow: what it is / how it behaves / how it connects
• One-click versioning with real commit messages
• Unified test history + side-by-side comparisons

One agent. One timeline. Continuous, traceable evolution.
Not 27 copies with “final” in the name.

And yes, create voice agents in minutes and mail us a screenshot on support@futureagi.com. Free $100 voucher guaranteed.

Get making with free credits -> https://shorturl.at/D35Qp

Fix Your AI Agent: Weekly Debugging AMA (RAG, Voice, Copilot, Text2SQL)

Priyam — Tue, 02 Dec 2025 16:58:56 +0000

Hey devs 👋

If you’re building agentic systems (RAG, Voice, copilots, chat agents, Text2SQL, etc.), you’ve probably hit some of these:

“It works on the eval set, melts down on real users.”
“Logs show nothing obvious, but the agent clearly did something dumb.”
“We can’t tell why it picked that tool / branch / answer.”

So for December, we’re running a weekly series:

Fix your Agent - AMA with Future AGI’s engineering team

What it is

Live, open office hours with our Senior Applied Scientist (Rishav) and ML Engineer (Kartik) where we walk through your problems, not slides.

We’ll cover things like:

Agent debugging & failure analysis
How to design evals & metrics for agents (not just single LLM calls)
Prompt optimization strategies that are actually measurable
Agent observability: traces, decision paths, loop detection
Architecture trade-offs for production systems (latency, cost, reliability)

Who it’s for

Backend / ML / data engineers shipping agentic features
Product folks responsible for reliability and UX
Anyone trying to move from “demo” to “production” with agents

🗓 When: Every Wednesday in December
🕤 Time: 9:30 AM PT
📍 Where: Zoom (via Luma)
🔗 RSVP link: https://luma.com/rekjbyfc

Come with:

A short description of your setup (stack, provider, agent type)
One or two specific failure cases or questions
Any logs / traces / sample conversations you can share (sanitized)

We’ll try to cover as many real examples as possible and share patterns that others can reuse.

If you’re planning to join, fill the form so we can prep for your questions and prioritize -> https://forms.gle/gbUZgeFbVsTccVoj8

Flow Analysis for Voice Agents: Turning Debugging into an Engineering Task

Priyam — Mon, 01 Dec 2025 18:14:24 +0000

If you’ve worked with voice agents, this might sound familiar:

You run a big batch of tests in your simulator.
Some calls fail in odd ways.
You open the workflow graph and start replaying calls, node by node, trying to find the moment the agent “went off script.”

PMs and engineers using SIMULATE were doing this all the time.

The question was always the same:
“Where exactly did this agent’s path diverge from what we designed?”

The process was slow, manual, and repetitive but also extremely valuable.

So we shipped it as a feature: Flow Analysis.

With Flow Analysis, each test run gives you:

Full path trace: the exact route an agent took through your workflow
Divergence point: the node where it broke away from the expected path
Conversation context: how the rest of the interaction unfolded from that point

This turns debugging from “scrub and guess” into a clear, visual diff between expected vs actual behavior.

Instead of hunting through graphs, you can focus on:

Fixing misrouted branches
Adjusting conditions or thresholds
Improving prompts and error handling where it actually matters

If you’re building or testing voice agents and still doing manual graph forensics, Flow Analysis might save you a lot of time.

🔗 More details: https://shorturl.at/Ia2tG