Multi-agent AI systems coordinate multiple specialized agents to solve problems that are too complex for a single model. Posts here explore supervisor-worker topologies, agent lifecycle management, tool governance, security envelopes, and the operational patterns required to run multi-agent systems in production.
13 posts tagged with multi-agent ai. ← All posts
The philosophy, trade-offs, and what we learned converting 18+ agents in 3 months.
The philosophy, trade-offs, and what we learned converting 18+ agents in 3 months. Provider abstraction as the foundation for portable agents.
How to instrument multi-agent systems with OpenTelemetry, propagate trace context across an in-memory bus, and build a layered evaluation pipeline — from real-time policy gates to async LLM-as-judge to SLO-based trust scoring. Everything I learned building Genie.
Every Professional Cloud Security Engineer exam bullet, mapped to a file path in an RBI FREE-AI aligned Go platform. Where the implementation matches, where the analog substitutes, and where the honest gaps are.
The mental model that says no two adjacent layers share a single point of failure for the same class of attack. From TLS to OTel, the eleven layers a customer request crosses before an answer comes back.
Board policy as a YAML file the risk team owns. Annexure VI as a database query. Every governance recommendation rendered as a file path in a Go repository.
Twelve months of running multi-agent AI in a regulated context. SLIs that matter, the incident runbook, drift detection, continuous adversarial testing, secret rotation, compliance posture as code.
Stdlib over libraries, single binary over framework, fail-closed defaults over forgiveness. The boring-on-purpose case for choosing Go to ship a multi-agent system into a regulated environment.
Fallback agents plus a CI step that replaces the primary agent with one that always errors. If the fallback doesn't produce a usable answer, the PR can't merge.
PAN check-digit validation, Aadhaar offline KYC, DigiLocker, PEP/sanctions — all in Go code, not in a prompt. The LLM's job is to translate the verdict into something a human can read.
Field notes from running multi-agent AI on K8s. The patterns the book recommends, the ones that survived contact with production, and the ones that broke in interesting ways.
Microsoft's Multi-Agent Reference Architecture in Go. Protocol, registry, bus, governance, orchestration, observability, evaluation — and how the seven hold each other up.
Multi-agent stacks have state: vector indexes, chat histories, agent memory. GKE for AI workloads needs StatefulSets, PVCs, gateway controllers, and the patterns that work in 2026.
An honest retrospective on the open-source Genie project after a year. The patterns that held up; the ones we rebuilt; the code we deleted because it solved problems we didn't actually have.
All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.