What is agentic AI?+
Agentic AI describes AI systems that act autonomously inside workflows — observing environment state, reasoning about goals, deciding on actions, and executing through tools and APIs. They differ from chatbots, which respond to questions, and from generative AI, which produces outputs on request. Agentic AI carries operational responsibility.
What is the difference between agentic AI and generative AI?+
Generative AI creates content (text, image, code) in response to prompts. Agentic AI acts on environments — it takes actions, calls tools, runs workflows, and pursues goals across multiple steps. Generative AI is a component agentic AI uses; agentic AI is the system around it.
What are the components of an agentic AI system?+
An agentic AI system has four core components: (1) an observation layer that ingests environment state; (2) a reasoning layer that interprets observations against goals; (3) a decision layer that selects an action; (4) an action layer that executes through tools, APIs, or human-in-the-loop handoff. Production systems additionally include evaluation harnesses, observability, guardrails, and audit logs.
We are interested in agentic AI but not sure whether we are ready to deploy it — where do we start?+
Our discovery and feasibility stage is a two-week engagement scoped to answer exactly that: what decision or workflow would be automated, what the human-approval thresholds should be, what the regulatory risk profile looks like, and whether the data and integration foundations are in place. The output is a written feasibility brief and agent-design spec — not a commitment to build. Many regulated-enterprise buyers find that starting with feasibility is the right first step before committing to a full delivery engagement.
How long does an agentic AI deployment take?+
A typical first deployment with Sense7ai takes 12–20 weeks from discovery to production: discovery and feasibility (2 weeks), design (2–3 weeks), build and evaluate (6–10 weeks), deploy and operate (2–4 weeks). Complex multi-agent systems and regulated-industry engagements extend the build phase due to validation evidence and regulatory review.
Can agentic AI systems be made auditable for regulated industries?+
Yes. Every Sense7ai-built agent emits a structured decision trail including inputs, reasoning steps, tool calls, outputs, and confidence indicators. These traces are retained per the customer's records-retention requirements and are designed to satisfy regulator review under FFIEC, GLBA, HIPAA, FDA 21 CFR Part 11, and EU Annex 11 standards.
What guardrails do you implement around agent actions?+
Tiered guardrails: (1) input validation and PII redaction; (2) tool whitelisting with parameter-level constraints; (3) action thresholds requiring human approval for monetary actions, data deletions, and regulated decisions; (4) prompt injection defenses; (5) kill-switches for emergency containment; (6) continuous output monitoring with drift detection and alerting.
How do you evaluate agentic AI before production?+
Evaluation runs at three levels: unit evals on individual tools and prompts; integration evals on end-to-end agent behaviour against a defined benchmark suite; adversarial evals for jailbreak resistance and prompt injection. Evaluations are designed before the first agent prompt is written and run on every sprint. We do not ship production agents without a passing eval suite.
Which LLMs do you use for agentic AI in production?+
There is no single best LLM. Frontier reasoning models (Claude Opus 4, GPT o3-series, Gemini 2.0 Pro) excel at complex planning and orchestration. Open-source models (Llama 3.x, Mistral, Qwen) win on data residency, cost at scale, and customisation. Production systems typically use a mixed-model strategy — a reasoning model for the orchestrator, smaller models for routine worker tasks — selected during the design phase.
What is Sense7ai's pricing and engagement model for agentic AI work?+
Engagements are fixed-scope under a written master services agreement. Commercials vary by deployment footprint (number of agents, infrastructure scale, on-premise vs cloud), the set of capabilities under contract, and timeline. A scoping call typically yields a written proposal in 5–7 business days. Sense7ai does not run single-sprint POCs or pay-per-prompt arrangements — every engagement targets a production system with full evaluation and observability instrumentation.
