The Architecture Behind a Hands-Free Productivity Layer for Engineering Teams
In today’s high-velocity software development environments, every second matters. Senior engineering leaders are constantly seeking ways to reduce friction in developer workflows — not just to save time, but to enhance focus, reduce context switching and improve team satisfaction. One such friction point? Ticket creation.
At Deliqt, we’ve built a voice-first AI agent that automates Jira ticket creation — a proof-of-concept system that senior leaders can study, replicate and customize. The goal is not just to replace typing with speaking, but to create a natural, intelligent interface layer that interacts with Jira the way humans think, not the way forms are structured.
In this post, we’ll show how it works, what the architecture looks like, and how your engineering org can implement a similar productivity layer — with Deliqt’s help.
Why This Matters for Engineering Leaders
Ask any developer or product manager: opening Jira, navigating projects, formatting tickets and remembering schema rules is far from joyful. While these are necessary tasks, they are low-leverage cognitive loads.
Now imagine this: “Create a bug: When I try special characters on the login page, it crashes. Mark high priority. Assign to backend.”
That’s all a user needs to say. The AI agent listens, understands the context, asks clarifying questions if needed and creates a properly formatted, fully categorized Jira issue. No typing, no toggling, no delay.
Outcome: Faster reporting. Better ticket hygiene. Zero context switching.
Inside the Architecture: What Powers the System
Building this system involved strategic integration of five core layers, each carefully selected to ensure modularity, scalability and enterprise extensibility.
1. Speech-to-Text: Deepgram
High-accuracy transcription is the foundation. We use Deepgram for real-time, low-latency speech recognition. In enterprise settings, this minimizes user frustration and increases adoption by ensuring near-perfect voice-to-text conversion.
2. NLP & Prompt Chaining: OpenAI
Once transcribed, the natural language input is parsed using OpenAI’s GPT models. This layer handles:
- Ticket type classification (bug, task, feature, etc.)
- Extraction of metadata (priority, team, assignee)
- Summary + description formatting
- Clarification, if critical fields are missing
Our prompt chaining strategy ensures the model works incrementally — first interpreting intent, then extracting fields and finally synthesizing structured data.
3. Workflow Orchestration: LangGraph
This is where real intelligence happens. LangGraph powers decision-making and multi-step workflows:
- Detects missing inputs and triggers clarifying questions
- Manages branching logic (e.g., “ask priority only if ticket is not a subtask”)
- Maintains memory across multiple turns of conversation
LangGraph gives the AI agent resilience in real-world scenarios, where inputs are imperfect and conversations are fragmented.
4. Jira Integration: REST API Layer
Once structured, data is passed to Jira using its REST API. We designed a pluggable adapter for this, allowing the same architecture to support other tools like Linear, Asana or Azure DevOps with minimal change.
5. Checkpointing: MongoDB
MongoDB stores conversational context and checkpoints. This enables:
- Stateful conversations (e.g., “Continue where we left off”)
- Recovery after disconnection or interruption
- Logging for compliance or analytics
By decoupling memory from runtime logic, we made the system stateless where it needs to scale and persistent where continuity is essential.
6. AI Observability: Tracing with Langfuse or LangSmith
In production-grade AI systems, visibility into the decision-making process is non-negotiable. We’ve integrated Langfuse (or LangSmith in some client implementations) to trace every interaction the AI agent performs — from the moment a user speaks, through transcription, reasoning, prompt routing, and final API action. This observability allows technical teams to inspect:
- The exact prompts generated at each step
- Intermediate thoughts and model decisions
- Token usage and latency at each layer
- How multi-step reasoning evolves over the course of a conversation
This is essential for debugging, optimizing, and auditing. For example, if a bug ticket was miscategorized as a task, Langfuse allows your team to replay and pinpoint the model misstep — including the context, routing logic, and model response. It’s like having a time machine for your AI workflows, enabling continuous learning and refinement.
7. Guardrails: Governance Without Sacrificing Flexibility
We’ve also built a robust system of AI guardrails, embedded throughout the workflow to maintain accuracy, compliance, and trust. These guardrails are rule-based and LLM-based, ensuring:
- Only permitted users can trigger certain ticket types or assign to specific teams
- Sensitive or ambiguous inputs are flagged for review
- Fallback logic engages when model confidence dips below a threshold
- Responses are moderated in real-time for policy violations or hallucinations
Our approach goes beyond static validation. We apply dynamic guardrails at multiple layers — post-transcription, post-NLU, and pre-API — allowing for progressive hardening without sacrificing conversational flow. This is particularly important for regulated environments or high-stakes workflows.
How We’re Prompting Differently
Unlike most implementations that either rely on a monolithic prompt or rigid schema templates, we embrace a dynamic chain-of-thought (CoT) design pattern. Each reasoning step — classification, field extraction, clarification, formatting — is treated as a composable, observable node in a flow graph.
But here’s the difference: we’re not just thinking step-by-step within the model; we’re staging thought externally across LangGraph nodes. This modular architecture allows us to:
- Retry specific reasoning steps without rerunning the entire conversation
- Swap in new CoT strategies (e.g., few-shot vs. zero-shot) at runtime
- Blend deterministic rules and probabilistic models for hybrid reasoning
This gives engineering leaders fine-grained control over both how the agent thinks and how transparent that thinking is. It also future-proofs the system for evolving foundation models, domain-specific LLMs, or model fine-tuning over time.
Strategic Benefits: Beyond Developer Delight
This isn’t just a hack to speed up ticketing — it’s a strategic productivity layer. Senior leaders should consider the broader impact:
- Faster throughput: Engineers log issues without breaking flow. QA teams reduce handoff latency.
- Standardized input: Every ticket follows company-wide formatting and routing rules.
- Hands-free logging: Ideal for teams in field ops, hardware testing or constrained environments.
- Accessibility built-in: Improves inclusivity for teams with diverse needs.
What Makes This Architecture Replicable
Several elements in our design make this a great candidate for enterprises to adopt or adapt:
- Modular tech stack (each layer is independently swappable)
- Workflow-centric design (LangGraph makes policies explicit)
- Multi-turn reasoning (enables intelligent clarifications)
- Security-aware API layer (ensures ticket creation adheres to org policies)
We deliberately avoided black-box shortcuts. Everything from token handling to user permissions can be customized.
Future-Ready: What Else You Can Build with This
The voice-to-Jira interface is just the beginning. The same pattern can be extended to:
- Update issue status or add comments via voice
- Voice-first sprint planning with integration to Miro or Confluence
- Auto-generate engineering documentation from spoken notes
- Add contextual logs to Slack, Notion or Google Drive
- It can also be configured as email auto-responder and meeting planner
- Enable multilingual voice agents for globally distributed teams
At Deliqt, we’ve already started consulting clients on similar extensions.
Want to Build This in Your Org?
This system is more than a demo — it’s a blueprint. If you’re a CTO, engineering head or digital transformation leader thinking about voice-driven automation, here’s how Deliqt can help:
- Discovery & Consulting: We evaluate your toolchain, workflows and compliance needs.
- Custom Implementation: From white-labeled voice agents to secure API bridges.
- Team Enablement: Documentation, onboarding and MLOps practices.
- Ongoing Support: For scaling, training and maintaining prompt quality.
Let’s Talk
This is how future-forward orgs reclaim time, reduce errors and build better developer experiences. If you’re interested in deploying a voice-native productivity layer tailored to your workflows, consult Deliqt. We’ll help you architect it, build it and scale it.
