Generative AI answers a prompt. An agent operates in a loop: it observes the current state, decides the next action — answer, call a tool, ask a clarifying question, or stop — executes it, and repeats until the goal is met or a stopping condition fires.
That difference is an engineering problem, not a prompt problem. An agent that can act needs a state model, scoped tools, an identity of its own, a defined failure path, and a log of everything it did. That is what we build around the model.
Our operating principle: the AI may talk, but rules decide. Wherever an outcome must be repeatable — payments, eligibility, clinical escalation, compliance checks — a deterministic layer owns the decision and the model owns the language.
A thin supervisor classifies intent and delegates to purpose-built specialist agents, each owning one domain with a documented contract. We have delivered orchestrators routing to 8+ specialist agents against a formal lifecycle state model, where exactly one agent is active at a time and no agent self-triggers.
One conversational surface, many journeys. Incoming requests are classified into distinct intents — factual Q&A, discovery, transactional planning, guidance, event-based — and routed to the agent and toolset built for that intent, instead of answering everything the same way.
Agents that read and write to your real systems through narrow, schema-validated tool contracts — internal APIs, databases, ticketing, payments, maps, third-party services — assembling live, priced, structured outputs your UI can render directly.
Low-latency speech-to-speech agents using Amazon Nova Sonic, removing the transcription and text-to-speech hops. Delivered in English, Hindi and Hinglish across text and voice — critical where the user base spans languages and levels of digital familiarity.
Every answer must cite an approved source — a knowledge base document, an active plan, or a system of record — before it reaches the user. Where grounding is unavailable, the agent declines or escalates rather than improvising.
Agents that don’t wait to be asked. Scheduled and event-triggered workflows on Amazon EventBridge and AWS Lambda drive follow-ups, reminders, drop-off recovery and adherence checks on the channel the user already uses — with hard stop conditions, not open-ended nagging.
A monitoring agent reads signals continuously and can suspend other workflows until a human reviews the case. High-impact or irreversible actions — financial transactions, external communications, safety-critical decisions — sit behind an explicit approval gate.
Agents that generate and validate domain-aware rules, produce SQL/PySpark against governed sources, execute checks across multiple data stores, and improve from expert feedback — with full audit logging on every generated artefact.
| DIMENSION | RULES-BASED AUTOMATION | GENERATIVE AI | AGENTIC AI |
|---|---|---|---|
| Core behaviour | Executes a fixed path | Responds to a single prompt | Plans, acts, observes, adapts across steps |
| Handles the unexpected | Fails or routes to a human | Answers, may improvise | Re-plans, selects a different tool, or escalates |
| Touches your systems | Yes, via hard-coded integrations | Rarely | Yes, via governed tool contracts |
| State | Stateless per run | Per conversation at best | Explicit state model with persisted transitions |
| Primary risk | Brittleness | Hallucination | Unsafe or over-permissive action |
| Controls needed | Testing, exception handling | Grounding, guardrails | All of the above, plus per-agent identity, tool-layer authorization, approval gates, action-level audit |
| What Rapyder adds | — | — | Deterministic orchestration, scoped tools, HITL gates, OTEL observability, online evaluation |
Agents can act, so the controls go beyond content filtering.
Evaluation tells you whether an agent works at release. Observability tells you whether it keeps working, and why it stopped. We treat both as mandatory.
Four telemetry layers, instrumented with OpenTelemetry from day one: the agent and framework layer (reasoning steps, model calls, tool invocations, agent handoffs), the service layer (memory reads and writes, gateway routing, identity and policy evaluations), infrastructure, and business-level application metrics.
What we instrument as standard
| Metric category | Examples we track and alert on |
|---|---|
| Latency | End-to-end P50/P95/P99; voice first-audio latency from end of utterance |
| Reliability | Runtime invocation errors, API 5xx rate, tool-call failure rate, session cleanup |
| AI quality | Goal success rate, groundedness, recommendation relevance, transcription accuracy |
| Safety | Guardrail intervention rate — alerting on over-blocking as well as under-blocking |
| Cost | Token consumption per session, per agent and per tool invocation |
| Escalation | Human-in-the-loop handoff volume and handoff success rate |
Evaluation runs in two modes: on-demand against a fixed test set before deployment, and online sampling and scoring of live production traffic afterwards, with explicit test cases per tool and per business rule — not only end-to-end conversation tests.
Where teams need visibility beyond logs, we build GenAI observability dashboards on CloudWatch, Athena and QuickSight that attribute latency, errors, token usage and cost down to the individual agent and tool.
Every engagement closes with a Runbook and an Implementation Document so your team can operate the platform independently.
| Pattern | Delivered outcome |
|---|---|
| Multi-agent care orchestration | 8 specialist agents plus one deterministic orchestrator across a ~17-state journey model; ≥90% red-flag detection recall with safety-first escalation to a clinician; designed for 45K interactions/month; P95 response under 45s across three languages |
| Intent-routed planning agent | 5 distinct intents routed to purpose-built agents and toolsets; 6 live API integrations assembled into priced day-wise plans; 30–40% lower inference cost on cached queries via semantic caching; designed for ~1,000 concurrent users |
| Dual-runtime voice + text support agent | RAG-grounded resolution across text and voice in English, Hindi and Hinglish; auto-ticket creation on unresolved and payment queries; managed autoscaling with no fixed capacity plan; voice first-audio within 2–3s of end of utterance |
| Zero-token transactional automation | Payment validation, top-ups, failures and refunds handled as deterministic templates — removing inference cost entirely from the highest-volume transactional path |
| Agentic data quality automation | Domain-aware validation rule generation with expert feedback loop and 100% audit logging of generated rules and executions |
Common Questions
An AI system that can reason over a goal, plan a sequence of steps, call tools or external systems to gather information and take action, and adapt its plan based on what comes back — largely without a human directing every step. The distinction that matters commercially is that it acts, so the engineering focus shifts from answer quality to safe, auditable action.
A chatbot answers. An agent completes a task: it looks up your payment status, drafts and sends the message, raises the ticket, books the slot. That means it needs permissions, a state model, an approval path for high-impact actions, and a log of everything it did — none of which a chatbot requires.
Usually one agent. A single tool-using agent is simplest to build, test and reason about, and it is the right default below roughly six to eight tools within one well-defined domain. Multi-agent earns its complexity when the domains are genuinely distinct, when a strict auditable sequence is required, or when sub-tasks can run concurrently. Multi-agent graphs add latency and debugging surface area, so we don’t reach for them by default.
Layered controls: narrow tool scopes, authorization re-validated at the tool layer rather than trusted from the model’s decision, least-privilege IAM per agent and per tool, guardrails on input and output, server-side override of any client-supplied prompt or tool configuration, an approval gate on high-impact actions, and audit logging of every tool call with its identity context.
Amazon Bedrock AgentCore as the default agent runtime — with its memory, gateway, identity, observability and evaluation services — Strands Agents as the default framework, Amazon Bedrock foundation models including Claude and Amazon Nova, Amazon Nova Sonic for voice, OpenSearch and Bedrock Knowledge Bases for retrieval, and Lambda, EventBridge, DynamoDB, API Gateway, Aurora PostgreSQL, S3 and CloudWatch across orchestration, state and observability.
Yes. AgentCore Gateway fronts existing Lambda functions and REST APIs as MCP-compatible tools without rewriting backend code. We define each tool as a narrow, schema-validated contract with documented inputs, output shape and failure modes.
Goal success rate, groundedness, human-intervention rate, tool-call failure rate, latency percentiles and cost per session — instrumented from day one and scored continuously against live traffic, not just against a pre-launch test set.
It depends on integration surface and risk level. Discovery and a governed proof of value typically move quickly; the time is usually spent on data access, tool contracts and approval design rather than on the agent itself. We scope this in the assessment.
