Automate multi-step workflows
Agents plan a task, call the right tools and APIs, and carry a process to completion, handling branching steps that a single model call cannot.
Build agentic AI systems that automate multi-step workflows and deliver reliable outcomes across your operations.
15-Minute Scoping CallAgentic AI vs generative AI: which is right for your project?
Agentic AI vs generative AI compared: how they differ, when to choose each and what it means for your build, from engineers who ship both.
- Autonomous agents tailored to your domain and workflows
- Secure orchestration across tools, systems, and data
- Production monitoring, governance, and performance tuning
Trusted by global innovators
What is the difference between agentic AI and generative AI?
Generative AI produces content, such as text, code or summaries, in response to a prompt. Agentic AI goes further: it plans a multi-step task, calls tools and APIs, and acts on the results, with a person approving the steps that matter. Choose generative AI for drafting and answers; choose agents for workflows.
Reliable, bounded execution
Retrieval grounding, tool constraints, and guardrails keep agents inside defined boundaries so outputs stay accurate and actions stay safe.
Human oversight where it matters
High-stakes steps are designed for review and approval, so automation speeds the work without removing accountability.
Orchestration across systems
Agents connect the tools, data sources, and services you already run, so automation spans your stack instead of living in one app.
What do we build with agentic AI?
Production-grade agent systems designed around your processes, with the orchestration, grounding, and oversight that make automation trustworthy.
Workflow automation agents
Agents that carry a multi-step business process end to end, gathering inputs, taking actions, and handing off when a human is needed.
Tool & API orchestration
Agents that call your internal tools, databases, and third-party APIs with scoped permissions and structured, checkable outputs.
Retrieval-grounded reasoning
Retrieval over your own knowledge and data so agent decisions are grounded in approved context, reducing hallucination risk.
Multi-agent systems
Coordinated agents that split a complex job into specialized roles and combine their results into a single reliable outcome.
Guardrails & evaluation
Boundaries, approval steps, and automated evaluation so agent behavior stays inside defined limits before and after release.
Monitoring & optimization
Tracing, logging, and performance tuning that keep agents observable and improving once they are running in production.
Which workflow would you automate with agentic AI first?
Have a multi-step process worth automating? Let's scope an agent that plans, calls your tools, and completes the work with oversight built in.
contact@agnotic.com
Partnerships
contact@agnotic.com
Facts and sources
Which frameworks apply to agentic and generative AI?
Short facts that shape how we build both, each linked to its primary source.
The NIST AI Risk Management Framework is a voluntary framework that organizes AI risk work into four functions: Govern, Map, Measure and Manage.
Source: NIST AI 100-1In July 2024, NIST released the Generative AI Profile (NIST AI 600-1), a companion to the AI RMF that helps organizations identify risks specific to generative AI and proposes actions to manage them.
Source: NIST AI 600-1OWASP defines excessive agency as the vulnerability that lets an LLM application take damaging actions in response to unexpected, ambiguous or manipulated model output, usually caused by excessive functionality, permissions or autonomy.
Source: OWASP GenAI Security Project, LLM06:2025
What do buyers ask about agentic AI?
Generative AI is usually cheaper per task: one model call, one answer. Agents make several model and tool calls per task, so cost depends on how many steps they take. We set step limits and measure cost per completed task.
Generative AI features, such as drafting or summarizing, are usually faster to ship. Agents take longer because each tool needs permissions, tests and a fallback when it fails.
Yes, and most systems do. Agents use generative models to read and write text, then add planning, tool calls and approvals around them.
Not if the first build keeps prompts, retrieval and evaluation in reusable services. Agents can then call the same pieces as tools.
Neither by name. They look at risk controls: the NIST AI Risk Management Framework organizes this work into Govern, Map, Measure and Manage, and when PHI is involved HIPAA requires a business associate agreement with each vendor that handles it. Agents need more logging and approval steps because they take actions.
Generative AI suits healthcare tasks that end in a draft a person reviews, such as a visit note or a reply to a patient message. An agent is needed when the task reads from several systems and writes a result back, such as referral intake, assembling a prior authorization packet or rescheduling an appointment. Each write step gets a named human checkpoint.
A visit note draft reads the visit transcript and writes nothing until the clinician signs. A patient message reply draft reads the message and a chart summary, and staff send it. Referral intake reads the incoming referral and creates a work item that staff confirm. A prior authorization packet pulls payer rules and chart documents, and a coordinator approves it before submission. Rescheduling reads open slots and books one within rules the practice sets, and the patient confirms the new time.
Related AI services
We Are Technology-Agnostic
With a diverse technology stack, we deliver solutions using a technology-Agnostic approach to meet your unique needs.
Wireframe & Ideation
User Experience
Real-Time Projects
Traditional Development vs. AI-Assisted Engineering
By adopting AI-assisted development, you can significantly reduce team size and development costs while maintaining the same speed, quality, and reliability of delivery.
Traditional development team
FTE
Business AnalystSolution ArchitectUI/UX DesignerProject ManagerBack-end EngineerFront-end EngineerQuality Assurance
- Manual coding and rule-based logic for each feature
- Full-time development team including all key roles
- Predefined workflows and human decision-making
AI-assisted engineering team
FTE
Solution ArchitectBack-end EngineerFront-end EngineerBusiness Analyst
Part time
UI/UX DesignerProject ManagerQuality Assurance
AI Agents
PlanningDesignCodeCI/CDTesting
- Orchestrating AI agents to automate coding and optimization
- Lean team using AI agents to expand development capacity
- Faster delivery with adaptive, data-driven improvement
AI Tools We Use for Software Engineering
We use AI-powered tools and automation frameworks to speed up every stage of the software development lifecycle.
Coding copilots
AI tools that assist engineers with code generation, refactoring, and documentation through contextual suggestions and inline reasoning.
AI agent frameworks
Multi-agent frameworks that enable coding assistants and context-aware workflow orchestration across CI/CD pipelines.
Data analysis and big data processing
Tools and systems for processing, analyzing, and transforming large unstructured datasets.
Infrastructure and orchestration platforms
Cloud and containerized environments that host, secure, and monitor Artificial Intelligence-assisted workflows.
Voice of success
We don't just build products; we forge lasting partnerships. See how we've helped industry leaders transform their vision into technical reality.
“I can clearly see how Agnotic has a unique way of handling end-to-end development. They are always active on quick chat and provide support quickly.”
Aaron Phelan
Founder, Benchmark