Agentic Systems in Enterprise
Agentic systems are AI‑driven agents that autonomously coordinate tools, APIs, and data sources to complete complex, multi‑step tasks. Netomi’s platform demonstrates how to deliver these agents at scale while meeting enterprise reliability, latency, and compliance requirements.
Architecture & Logic
Netomi’s architecture places large language models (LLMs) at the core of a governed orchestration pipeline. GPT‑4.1 handles low‑latency reasoning and deterministic tool calls; GPT‑5.2 performs deeper, multi‑step planning when needed. A runtime governance layer validates every interaction against schemas, policies, and privacy rules.
Lesson 1: Build for Real‑World Complexity
Syntax – Agentic Prompting Patterns
Use structured prompts that embed the following elements:
- Persistence reminders – keep context across turns.
- Explicit tool‑use expectations – force the model to call a tool for factual data.
- Structured planning – ask the model to outline steps before execution.
- Rich‑media decision cues – signal when images, videos, or forms are required.
Parameters
- max_tokens: limit output length to avoid runaway generation.
- temperature: set to 0 for deterministic tool calls; higher values only for creative drafting.
- tool_call_threshold: confidence cutoff that triggers a tool call.
Edge Cases
- When intent confidence is low, route to a fallback flow instead of free‑form generation.
- Handle contradictory data by prioritizing the most recent source and logging the conflict.
- Detect missing arguments early via schema validation to prevent downstream failures.
Lesson 2: Parallelize Everything to Meet Latency Expectations
Syntax – Concurrency Blueprint
Define independent sub‑tasks that can run in parallel streams. Example pseudo‑code:
parallel {
classify_intent();
retrieve_customer_profile();
fetch_policy_rules();
}
await all;
execute_plan();
Parameters
- stream: enable token‑level streaming for immediate feedback.
- concurrency_limit: cap parallel calls to protect downstream services.
- latency_budget_ms: abort or fallback if total response exceeds budget.
Edge Cases
- Partial failures: if one parallel branch times out, proceed with available data and annotate the response.
- Resource saturation: dynamically throttle concurrency based on system load metrics.
- Order‑dependency: enforce ordering only when data from one branch is required by another.
Lesson 3: Make Governance an Intrinsic Part of the Runtime
Syntax – Governance Ruleset
Governance policies are expressed as declarative JSON that the runtime evaluates before any model output is sent to the user.
{
"schema_validation": true,
"policy_filters": ["brand_prohibited_terms", "regulatory_topics"],
"pii_masking": true,
"fallback_behavior": "safe_response"
}
Parameters
- confidence_threshold: minimum intent confidence to allow free‑form generation.
- audit_log_enabled: toggle detailed token and tool‑call tracing.
- fallback_strategy: choose between "safe_response", "human_handoff", or "retry".
Edge Cases
- Policy conflict: prioritize the most restrictive rule and raise an alert.
- PII detection false positives: log for review but continue with masked output.
- Deterministic fallback loops: enforce a maximum number of fallback attempts to avoid infinite recursion.