One workflow, fully decomposed: tasks, autonomy levels per step, and a clear line between what the agent owns and where a human stays in the loop. A working design reference, not a deck.
Launch your AI agent
Ideal for:
- Teams with a single workflow and a clearly defined system context
- Organizations that need quick validation and clear ROI before committing to a broader rollout
- Teams that want production-ready AI without the overhead of a full-scale deployment
Rollout duration:
- Typically 2-4 weeks, aligned with your team’s capacity
- Planning, workflow mapping, and agent setup
- System integrations and testing
- Report with ROI summary and scale recommendations
What you get:
Workflow mappingWorkflow mapping
System designSystem design
Tool calling, orchestration, fallback logic, inter-agent handoffs — the full architecture. Built as a multi-agent system from the start, so it doesn’t collapse the moment you need to add a second agent or a parallel branch.
System integrationsSystem integrations
Live connections to 1–2 systems you already run — Jira, GitHub, ServiceNow. Tested against real data, not mocked. Agents work with actual inputs before delivery.
Evaluation harnessEvaluation harness
Baseline metrics, target KPIs, and automated tests across task completion, latency, and escalation rate. If you can’t put a number in front of leadership, the work isn’t complete. This is how you make the case.
GuardrailsGuardrails
Logging, approval gates, and policy rules that define what agents act on autonomously and what gets escalated. Every action traceable. No black-box behavior, no compliance surprises.
ReportReport
What ran, what the numbers showed, and what scale looks like next. Written for both the team that built it and the stakeholders deciding whether to expand it.
Built by practitioners
Some of our AI work happens in delivery. Some ends up at agentic AI workshops we run at industry events.
If you’re building something ambitious, talk to people who work with this every day.
Our agentic AI lineup
Engineering productivity
Task:
Automate core engineering processes including pull request summarization, ticket refinement, test case generation, bug triage, and code migration suggestions.
Systems involved:
GitHub / GitLab, Jira, Confluence, Slack
Control and evaluation:
All actions are logged and fully auditable. Agent behavior is monitored continuously, with performance metrics tracked against developer throughput baselines set at the start.
More qualified leads. Less manual calling
AVER uses Austra Nova to automate calls and qualify leads, freeing its sales team to focus on the strongest prospects. →
Customer feedback you can actually use
AI-powered analytics turns millions of reviews into trends, insights, and answers teams can explore through an AI agent. →
The hidden risks of mass AI agents
Some agents look impressive in demos but struggle when deployed in real-world enterprise or SMB environments. Without proper controls, evaluation, and integration, agent deployment often stalls, costs rise, and security or compliance risks emerge.
Look good (in demos)
Many AI agents shine in demos but fail in real workflows with multiple systems and unpredictable inputs. Without proper controls, they don’t hold up in production.
- Agents may handle test cases well but fail on edge cases or unexpected user behavior
- Lack of fallback logic or error handling can disrupt workflows
- Teams often underestimate the complexity of real-world integration
Predictable, auditable, outcome-focused agentic AI
Automate workflows end-to-end
Achieve measurable success rates across tasks and maintain safe autonomy levels, so your teams can focus on higher-value work.
Reduce handle time and backlog
Cut manual work, accelerate processing, and streamline operations with intelligent automation.
Auditable and predictable behavior
All agent actions are logged, monitored, and controlled for consistent and reviewable performance.
Controlled costs per task
Monitor task-level resource usage and optimize prompt and tool execution to maintain predictable costs.
Performance evaluation
Use evaluation harnesses, regression tests, and quality metrics to improve agents over time.
Safe autonomy
Agents operate within defined permissions and policies, with full integration and real-time observability.
Pick offerings to fit your operations
Unsure what to build
Explore AI ROI Discovery and Smart Adoption to identify high-impact opportunities and understand the value of AI before committing.
Complex R&D
Collaborate with our engineers to conduct research and develop bespoke AI systems from prototype to full-fledged solutions.
Autonomous calling platform
Put Austra Nova on your phones for outbound and inbound calls, live qualification, and CRM write-back, without a rep on the line.
Ready-made voice agent
Launch a Voiager assistant to conduct interviews and produce actionable analytics.
How we build AI agents that survive production
Defined success metrics
Each agent starts with measurable targets such as task success rate, escalation rate, latency, and cost per task to validate performance.
Evaluation frameworks
Structured evaluation frameworks test reasoning, tool usage, task completion, and failure handling across different scenarios.
Benchmark testing
Agents run against benchmark-style task suites that simulate real workflows to identify edge cases and verify reliability.
LLM-as-a-judge evaluation
Automated scoring uses language models to assess outputs for correctness, completeness, and relevance at scale.
Production monitoring
Telemetry, task analytics, and regression tests continuously track agent performance after deployment.
Interactive evaluation
Controlled environments simulate real integrations so teams can observe behavior and validate improvements before release.
Controlled and auditable AI agents
Built for security, compliance, and operational control, our agents give enterprises, startups, and SMBs a clear path to production AI, without the governance gaps that stall procurement or create audit exposure.
Governance model
Define who controls each agent and what actions are permitted, down to the role and task level.
Permission policies, enforcement rules, and immutable audit logs make agent behavior traceable and defensible. Built to satisfy the access control and accountability requirements that SOC 2 Type II and ISO 27001 audits examine by default.
Data and privacy model
Sensitive data is strictly scoped to what each agent actually needs.
PII safeguards, anonymization, and enforced data boundaries keep deployments aligned with GDPR Article 25 (data minimization by design) and HIPAA’s minimum necessary standard.
Deployment options
Flexible deployment supports SaaS, on-prem, or hybrid models.
Operational boundaries and infrastructure choices allow companies of any size to run agents securely within their existing environments.
Monitoring and observability
Real-time telemetry, performance metrics, error tracking, and alerts provide continuous visibility into agent operations.
Teams can monitor, audit, and intervene whenever necessary to maintain control and compliance. Logs are structured for SIEM export and formatted to satisfy SOC 2, ISO 27001, and internal audit requirements out of the box.
FAQ
We define clear success metrics for each workflow, including task success rate, latency, and escalation rate. Our autonomous AI agents development services include continuous monitoring, offline tests, and regression checks to make sure agents meet these targets before and during production.
Through our custom AI agents development, systems operate within defined permissions, policy controls, and approved action sets. Audit logs and exception alerts provide predictable, auditable behavior aligned with enterprise governance standards.
As a custom AI agents development company, we make sure agents are restricted to authorized tools only. Inputs are validated, and unsafe prompts trigger alerts or fallback to human review to prevent misuse and maintain security.
Task-level usage, tool access, and prompt execution are monitored in real time. Dynamic adjustments prevent cost overruns and keep performance efficient and output quality high.
Agents connect to systems like Jira, GitHub, GitLab, Confluence, Slack, HubSpot, ServiceNow, and custom APIs, depending on workflow needs.
Agents access only the data required for their workflows. Sensitive information is protected with boundaries, anonymization, and PII safeguards to maintain compliance.
Yes. Our agents connect to enterprise systems such as CRM, ERP, Slack, Jira, HubSpot, ServiceNow, and custom APIs, providing seamless workflow automation and keeping permissions and security policies intact.
Our AI agents development services deliver solutions that can operate autonomously within defined boundaries or work alongside humans with human-in-the-loop checkpoints, depending on workflow complexity, risk, and compliance requirements.
