AI Frameworks & Agent Orchestration - Semantic Kernel
Semantic Kernel is powerful and easy
to get wrong in production
We design and build Semantic Kernel agents that go beyond demos - wiring planners, plugins, memory stores, and process automation into the CRMs, ERPs, and data sources your team actually uses.
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Operators and teams we've worked with
Most Semantic Kernel builds stall between prototype and production deployment
Semantic Kernel's plugin architecture and kernel function model make it straightforward to get a working demo in a day or two. The problems surface at real workloads: planners that hallucinate tool sequences, memory configurations that retrieve the wrong context, plugin chains that fail silently when a downstream API returns something unexpected, and token budgets that blow up on longer conversations. Teams also underestimate what it takes to manage multiple kernel instances across services, version prompt templates safely, and connect the Process Framework to durable state outside the .NET or Python process boundary. Without careful design, what started as an agent becomes a fragile script engineers fear to touch.
Revenue Institute has run these implementations end to end. We audit your kernel configuration and plugin inventory, redesign the planner and memory strategy for your actual data volumes, and build the observability layer - logging kernel events, tracking plan steps, surfacing failures - that lets your team operate the agent after handoff. We also handle the integration work Semantic Kernel does not do for you: authentication flows, rate-limit handling, and connecting kernel outputs to the downstream systems where the work needs to land.
What we do with Semantic Kernel
What we build inside your Semantic Kernel environment
Plugin design and kernel function architecture
Semantic Kernel plugins are the unit of capability your agent calls. We design plugin boundaries so each kernel function does one thing cleanly, handles errors explicitly, and returns structured output the planner can reason about. Poorly scoped plugins derail plans.
Planner selection and prompt strategy
Semantic Kernel offers multiple planners - Handlebars, the stepwise planner, automatic function calling - plus the Process Framework for durable orchestration. We match the planner to your task complexity, version the prompt templates guiding plan generation, and validate plans before they hit live systems.
Memory and vector store integration
Semantic Kernel's memory connectors support Azure AI Search, Qdrant, Chroma, and others. Getting retrieval right means chunking strategy, embedding selection, metadata filtering, and knowing when to use semantic search versus structured lookup - so the agent gets relevant context without flooding the window.
Multi-agent and process orchestration
Semantic Kernel's Agent Framework and Process Framework let you compose specialized agents into a coordinated workflow. We design the agent topology, define handoff contracts between agents, and wire durable state so a multi-step process survives restarts, timeouts, and partial failures.
Observability, logging, and cost controls
Semantic Kernel exposes kernel events and filter hooks most teams never configure. We instrument your kernel to log plan steps, plugin calls, token consumption, and latency at each stage - feeding dashboards your team uses to spot regressions, control spend, and diagnose failures.
CRM, ERP, and line-of-business integration
An agent that only touches a sandbox is not useful. We build the plugins and connectors that tie Semantic Kernel to HubSpot, Salesforce, Dynamics, or NetSuite - handling authentication, pagination, schema mapping, and write-back so it produces outcomes in your real systems.
Our framework
How a Semantic Kernel engagement runs
Audit and architecture
We start by reviewing your existing kernel setup, plugin inventory, and the process you are automating. If you are starting from scratch, we map the process first. Output is an architecture document covering planner choice, plugin boundaries, memory strategy, and integration points.
Build and integration
We build the plugins, configure the kernel, wire the memory store, and connect your target systems. Every plugin is tested in isolation before joining the plan graph. We run the planner against realistic inputs and iterate on prompts until quality is consistent.
Handoff and operate
We document the architecture, prompt templates, and plugin contracts so your team can maintain and extend the agent. We configure observability, run a knowledge transfer, and stay available afterward. The goal is an agent your engineers own, not a black box.
What Semantic Kernel actually does well and where it breaks down in practice
Semantic Kernel is Microsoft's open-source SDK for building AI agents and orchestrated LLM workflows in .NET and Python. Its core abstraction is the kernel - a runtime that manages plugins, memory, and model connectors - combined with a planner that decomposes a goal into a sequence of plugin calls. The plugin model is well-designed: each plugin exposes typed functions with descriptions the planner uses to decide what to call and when, an advantage over looser approaches where tools are defined only in a system prompt. The Process Framework lets you define multi-step processes with explicit state transitions - important for anything that must survive a restart or coordinate work across agents over time.
The failure modes are predictable once you have seen a few implementations. Planner instability is most common: given too many plugins, ambiguous function descriptions, or underspecified goals, it generates plans that look plausible but call functions in the wrong order or with incorrect arguments. Memory retrieval is second: teams configure a vector store, embed a large corpus, then find the agent retrieves tangentially related content that fills the context window without helping. The third is silent plugin errors - Semantic Kernel catches exceptions from plugin calls, but if your error handling returns a generic failure message, the planner often continues anyway and produces a confident-sounding wrong answer. None of these are framework bugs; they are design and configuration problems requiring deliberate attention.
What production-grade Semantic Kernel implementation looks like in a mid-market operation
Mid-market companies typically come to Semantic Kernel with a specific problem: automating a research and summarization workflow, building an agent that queries internal data and drafts responses, or orchestrating a multi-step approval process that lives in email. The right implementation starts with that process, not the framework. You map the steps a human takes, identify which require judgment versus which are mechanical, and design plugins that match the mechanical steps cleanly. The planner handles sequencing; your plugins handle execution. When that boundary is clear, the system is maintainable. When it is blurry, you get plugins that are too broad and a planner that cannot reason about them reliably.
Connecting Semantic Kernel to real business systems is where most integration work lives. The framework does not ship with connectors to Salesforce, HubSpot, NetSuite, or most ERP systems - you build those as plugins, which means handling OAuth flows, managing API rate limits, mapping your internal data model to the external API's schema, and surfacing errors to the planner in a way that allows recovery rather than silent failure. Done well, this layer makes the agent useful rather than a sophisticated toy. Done poorly, it is the source of most production incidents. Revenue Institute has built this layer across enough platforms and industries to know where the edge cases live before they show up in your production logs.
We're vendor-agnostic
Other AI Frameworks & Agent Orchestration platforms we specialize in
Not sure Semantic Kernel is the right fit? We implement and optimize these too - and we'll tell you honestly which one fits your business.
Semantic Kernel questions, answered
Why use Semantic Kernel instead of LangChain or a simpler orchestration library?
Semantic Kernel is the right choice when your team is primarily .NET or Python, you are already in the Microsoft Azure ecosystem, or you need the Process Framework's durable workflow model. It has strong typing, first-class plugin contracts, and deep integration with Azure OpenAI and Azure AI Search. LangChain has a larger open-source ecosystem but looser abstractions that can make production debugging harder. The honest answer is that the right framework depends on your stack and your team's skills, not on which one has more GitHub stars.
We already have a Semantic Kernel prototype. Can you rescue it rather than rebuild from scratch?
Yes, and that is often the faster path. We audit the existing codebase, identify the specific failure modes - usually planner instability, memory misconfiguration, or missing error handling - and fix them in place where practical. A full rebuild is only warranted when the plugin architecture is so tangled that incremental fixes would cost more than starting clean. We will tell you honestly which situation you are in after the audit.
How do you handle prompt template versioning and regression testing?
Semantic Kernel supports prompt templates defined in YAML with separate configuration files, which makes version control straightforward. We set up a testing harness that runs a fixed set of representative inputs through the planner after any template change and flags plan quality regressions before they reach production. This is not glamorous work, but it is what separates agents that stay reliable from ones that degrade quietly over time.
What does memory configuration actually involve - can we just point it at our existing database?
Not directly. Semantic Kernel's memory connectors work with vector stores, not relational databases. Getting useful retrieval means deciding what content to embed, how to chunk it, which embedding model to use, and how to filter results by metadata before semantic ranking. If your source of truth is a SQL database or a document repository, we build the ingestion pipeline that keeps the vector store current and configure the retrieval logic so the agent gets relevant context without noise.
How long does a typical Semantic Kernel engagement take?
A focused single-agent build with two to four plugins and one integration target typically takes four to eight weeks from architecture sign-off to go-live. Multi-agent orchestration or complex process automation with multiple line-of-business integrations takes longer. We scope based on your specific process, not a generic estimate. If your timeline is shorter, we will tell you what is realistic and what would have to be deferred.
Do you work with the Python SDK or only the .NET version?
We work with both. The Python SDK has reached feature parity with .NET for most production scenarios including the Agent Framework and the core planner types. The .NET SDK still leads on the Process Framework's durable workflow capabilities. We will recommend the language that fits your team's existing skills and the deployment environment you are targeting.
What happens after the agent is live - who maintains it?
The goal of every engagement is an agent your team owns. We document the architecture, plugin contracts, and prompt templates in a format your engineers can actually use. We configure logging and alerting so your team knows when something breaks. We offer a defined post-launch support period for issues that surface in production. After that, your team runs it - or you bring us back for the next capability you want to add.
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