The Good Tech Companies - The Last Mile, Solved Where It Matters: Domain-Native Agentic AI by Praveen Satyanarayana

Episode Date: October 1, 2025

This story was originally published on HackerNoon at: https://hackernoon.com/the-last-mile-solved-where-it-matters-domain-native-agentic-ai-by-praveen-satyanarayana. Praveen Satyanarayana’s Milky Way agentic AI tackles last-mile analytics with domain-native ontologies, verification scaffolds, and audit-ready insights. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #praveen-satyanarayana-tredence, #milky-way-agentic-ai, #domain-native-ai-systems, #last-mile-analytics, #enterprise-knowledge-graphs, #ontology-driven-ai, #ai-verification-scaffolds, #good-company, and more. This story was written by: @kashvipandey. Learn more about this writer by checking @kashvipandey's about page, and for more stories, please visit hackernoon.com. Praveen Satyanarayana, Head of Engineering at Tredence, built Milky Way, a domain-native agentic AI system for last-mile analytics. By grounding business terms in ontologies, enforcing dual-judge verification, and providing auditable decision narratives, Milky Way delivers reliable, scalable insights across retail, BFSI, supply chain, telecom, and healthcare domains.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. The Last Mile, solved where it matters. Domain Native Agentic AI by Praveen Satynaariana by Kushvi Pondi. Enterprises do not need a single, universal AI bot. They need domain native agent systems that understand retail baskets and promotions, bank deposits and risk flags, skew velocity and supplier OTIF, network cells and outage cohorts, patient journeys and protocol deviations. That is the center of Praveen Saddianariana's vision at Treadence, custom workflows, ontologies, knowledge graphs, tools, and metrics for each domain, carried by two non-negotiables that apply everywhere.

Ontology first grounding, business language resolves to canonical metrics, permitted joins, units, and lineage, and the system locks scope across metric, time, segment, and geography before it spends any compute. Reliability over autonomy, multi-turn reasoning runs through a verification. scaffold with dual judges, one LLM critic for structure and clarity, and one gold data layer for numerical truth. For more than a decade companies have poured money into warehouses and dashboards while decision latency stayed stubborn. The last mile problem is not a visualization gap. It is a reasoning gap. Closing it requires systems that clarify intent, form and test hypotheses, map claims to govern data, and return decision-ready recommendations with an auditable trace. That is the point of Milky Way, the agentic decision system Praveen and team have built four

descriptive and diagnostic analytics. It treats enterprise reality as it is, with overloaded terms, partial data, brittle joins, and audit demands. In retail, an agent that cannot speak basket, UPC, promo, and Storeweek has no business writing sequel. Preveen Satyana, we build constellations of agents, not a mascot bot. Each one knows its domain, its tools, and and its guardrails. Praveen's Satunaryana what makes this different. Agentic AI is software that chooses actions and uses tools to pursue a goal within guardrails. Praveen's contribution is to make that idea measurable and governable for analytics. The architecture is simple to state and strict to implement. 1. Ground the language, map terms to entities, metrics, synonyms, lineage, and admissible

join paths. Refuse ambiguity. 2. Scaffold the execution. Compile plan lands into guarded tool calls with timeouts, retries, circuit breakers, and SQL structure checks on schema variants. Three, treat hypotheses as objects, generate competing explanations, bind each to fields, joins, transforms, tests, and visuals, then ranked by prior likelihood, cost to validate, and expected information gain. Four, judge twice. Let a critic model score clarity and coverage while a gold store verifies numbers, joins, filters, and statistical claims. 5. Deliver a decision narrative, provide tables, figures, confidence, and links to the full trace for audit. Why now? Error compounding is unforgiving. Modest per step error rates

collapsing to end reliability in multi-step workflows, which is why bounded steps, verification, and human gates matter. Conversation length also drives token-castand latency, so practical systems favor short-state tasks with explicit checkpoints. Milky Way decomposes work into verifiable subplans and keeps context tight. Short, verified steps beat long, clever chats. Preveen Satyanariana, a crisp domain native playbook. The system does not ship one template. It ships domain packs that include an ontology and knowledge graph, a vetted toolset, a starter library of hypotheses, and acceptance metrics. Retail, BFSI, supply chain, telecom, healthcare, and travel all use the same backbone but install different packs. Joins and lineage differ by domain.

so reliability must be defined locally and enforced centrally. The fastest way to lose trust is to answer quickly with the wrong join data. Preveen Satyanariana knowledge graph and ontology operations. The ontology and knowledge graph act as the contract between language and data. They encode entity relationships, metric lineage, join admissibility, synonyms, and policy tags. They also carry path costs and quality labels so planners prefer short, reliable routes. Operations on this layer include, 1. Drift monitors, detect schema changes, definition shifts, unit mismatches, and relationship breaks. 2. Adaptors and Curricula. Provide domain adapters for new tables and curate curriculum tasks that harden weak spots.

3. Synonym and alias management. Maintain a compact term store supported by embeddings for recall and by hard rules for precision. 4. Join validators. Run pre-flight checks and structural sequel tests. on hidden schema variants before execution. 5. Lineage transparency, record tables, joins, filters, and aggregation rules in a trace that is explorable by role. Custom evaluations in rubrics. Generic leaderboards do not measure enterprise reliability. Milky Way use custom rubrics and acceptance tests that turn behavior into signals for learning and for go live gates. 1. Framing in guardrail signals, clarify count, scope lock precision, missing information requests,

task-type detection, and interrupt or override availability. 2. Ontology alignment signals, field mapping accuracy against a gold shortlist, join validity rate on the ontology graph, aggregation rule adherence to lineage, and escalation latency when required data is absent. 3. Plan and execution signals. Plan completeness, statistical test appropriateness, SQL structural success ratio, and exploratory depth across distributions, cohorts,

and controls. 4. Insight signals, causal attribution confidence, actionability lead time, persona fit for executive and analyst consumption, and trace transparency index. 5. Learning signals. Roll-shaped rewards that credit clarifiers for scope block improvements, mappers for field accuracy and join validity, executors for structural correctness, and reporters for persona fit and transparency, with a team bonus for on-time closure above confidence thresholds. These evaluations run offline on synthetic tasks that mirror real schema and run online as shadow or gated flows. How multi-turn reasoning actually runs. Clarification converges

to scope lock with minimal burden on the user. The hypothesis engine cedes candidates from a domain library and from retrieval over prior cases and marks coexistence or competition. The mapper binds each hypothesis to fields and joins and produces a factor map. The executor runs SQL land tests under timeouts and circuit breakers and tracks exploratory depth. The critic and gold judges iterate on narrative quality and numeric truth. Their porter assembles role-specific narratives with evidence, confidence, and next actions. Every stage emits metrics that feed both evaluation and reinforcement learning. Reliability and economics by design. The scaffold captures tool signatures, side effect policies, and costs. Tools return structured feedback that include success, partial success,

and cost. Destructive operations are gated. Memory is episodic and semantic rather than an endless transcript. Stateless tools are preferred where possible. Stateful agents use retrieval and short contexts to control token cost. Adoption that earns trust. Teams begin with human in the loop where analysts validate scope lock and first recommendations. They progress to human on the loop where routine paths auto run and exceptions require review. They then authorize selective autonomy for narrow, high confidence workflows with rollback and full audit. The sequence builds confidence without pausing impact. Open work, stated plainly, ontology and graph upkeep carry real cost, drift detection and domain curricular ongoing. Reward gaming is possible and must be checked with cross-rubric

audits and surprise variance. Synthetic to real gaps persist and benefit from targeted shadow runs on live incidents. Credit assignment in long traces is noisy, so role-shaped rewards and team bonuses improve stability. Why this vision is credible. Praveen's approach combines agentic orchestration, tool use, retrieval, and learning from signals, then anchors them to enterprise constraints. The stances opinionated where it must be with ontology gates and a gold judge and modular where it should be with swappable tools and domain adapters. If the last mile is about making analysis useful on time and under control, this is a path that holds up in production and scales by design. A narrative is only as strong as its trace. We ship the trace and the answers.

Praveen Satyanarayana references. 1. Oracle. What is Agentic AI? 2025. 2. Gartner. Top strategic technology trends for 2025. Agentic AI. 24. 3. Google Deep Mind. Introducing Gemini 2. 0 for the Agentic era, 24. 4. Utkarsh Kanwa. Why I am betting against AI agents in 2025, 2025. 5. Navine Chadha, AI First Professional Services. The Great Equalizer is Coming, 2025. 6. Industry coverage on agent rollouts in enterprise adoption, 2025. This story was distributed as a release by Kushvi Pondi under Hackernoon's business blogging program. Thank you for listening to this Hackernoon story, read by artificial intelligence. Visit hackernoon.com to read, write, learn and publish.