AI Frameworks & Agent Orchestration - Semantic Kernel
Semantic Kernel is powerful and easy
to get badly 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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$250M+
Pipeline generated
42%
Average pipeline growth
18.3%
Average budget saved
Results from actual client engagements.
Trusted by the teams we build 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 when you move toward real workloads: planners that hallucinate tool sequences, memory configurations that retrieve the wrong context at the wrong time, plugin chains that fail silently when a downstream API returns an unexpected response, 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 Semantic Kernel's process framework to durable state outside the .NET or Python process boundary. Without careful design, what started as an agent becomes a fragile script that engineers are afraid to touch.
Revenue Institute has run these implementations end to end. We audit your existing 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, and surfacing failures - that lets your team operate the agent after we hand it off. We also handle the integration work that Semantic Kernel itself does not do for you: authentication flows, rate-limit handling, and connecting kernel outputs to the downstream systems where the work actually 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 can call. We design plugin boundaries so each function does one thing cleanly, handles errors explicitly, and returns structured output the planner can reason about. Poorly scoped plugins are the most common reason Semantic Kernel plans go off-rails in production, and fixing them after the fact is expensive.
Planner selection and prompt strategy
Semantic Kernel offers multiple planning approaches - the Handlebars planner, the function-calling stepwise planner, and increasingly automatic function calling - plus the separate Process Framework for durable orchestration. Each has different failure modes and cost profiles. We match the planner to your task complexity, write and version the prompt templates that guide plan generation, and add validation steps that catch bad plans before they execute against live systems.
Memory and vector store integration
Semantic Kernel's memory connectors support Azure AI Search, Qdrant, Chroma, and others. Getting retrieval right means more than pointing at a vector store - it means chunking strategy, embedding model selection, metadata filtering, and deciding when to use semantic search versus structured lookup. We configure the full retrieval pipeline so the agent gets relevant context without flooding the context window.
Multi-agent and process orchestration
Semantic Kernel's Agent Framework and Process Framework let you compose multiple 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 can survive restarts, timeouts, and partial failures without losing work or looping indefinitely.
Observability, logging, and cost controls
Semantic Kernel exposes kernel events and filter hooks that most teams never configure. We instrument your kernel to log plan steps, plugin calls, token consumption, and latency at each stage. That data feeds dashboards your operations team can actually use to spot regressions, control spend, and diagnose failures without digging through raw LLM responses.
CRM, ERP, and line-of-business integration
An agent that only reads and writes to a sandbox is not useful. We build the plugins and connectors that tie Semantic Kernel to HubSpot, Salesforce, Dynamics, NetSuite, or whatever systems your business runs on - handling authentication, pagination, schema mapping, and write-back logic so the agent produces outcomes in the systems your team already works in.
Our framework
How a Semantic Kernel engagement runs
Audit and architecture
We start by reviewing your existing kernel setup, plugin inventory, and the business process you are trying to automate. If you are starting from scratch, we map the process first. Output is a concrete architecture document covering planner choice, plugin boundaries, memory strategy, and integration points - agreed before any code is written.
Build and integration
We build the plugins, configure the kernel, wire the memory store, and connect to your target systems. Every plugin is tested in isolation before being added to the plan graph. We run the planner against realistic inputs, not toy examples, and iterate on prompt templates until plan quality is consistent across the input variance your real users will produce.
Handoff and operate
We document the architecture, prompt templates, and plugin contracts so your team can maintain and extend the agent. We configure the observability layer, run a structured knowledge transfer, and stay available for a defined support period after go-live. The goal is an agent your engineers own, not a black box they are afraid to modify.
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 can decompose a goal into a sequence of plugin calls. The plugin model is genuinely well-designed: each plugin exposes typed functions with descriptions that the planner uses to reason about what to call and when. This is a real advantage over looser orchestration approaches where the agent's available tools are defined only in a system prompt. The Process Framework, added more recently, lets you define multi-step business processes with explicit state transitions, which is important for anything that needs to survive a process restart or coordinate work across multiple agents over time.
The failure modes are predictable once you have seen a few implementations. Planner instability is the most common: when the planner is given too many plugins, ambiguous function descriptions, or goals that are underspecified, it generates plans that look plausible but call functions in the wrong order or with incorrect arguments. Memory retrieval is the second major failure point - teams configure a vector store, embed a large corpus, and then find that the agent retrieves tangentially related content that fills the context window without helping the model answer the actual question. The third failure mode is silent plugin errors: Semantic Kernel will catch exceptions from plugin calls, but if your error handling returns a generic failure message, the planner often tries to continue anyway and produces a confident-sounding wrong answer. None of these are framework bugs - they are design and configuration problems that require 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 operational problem: automating a research and summarization workflow, building an agent that can query internal data and draft responses, or orchestrating a multi-step approval process that currently lives in email. The right implementation starts with that process, not with the framework. You map the steps a human takes, identify which steps 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 end up with plugins that are too broad and a planner that cannot reason about them reliably.
Connecting Semantic Kernel to real business systems is where most of the 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 between your internal data model and the external API's schema, and deciding how to surface errors back to the planner in a way that allows recovery rather than silent failure. Done well, this integration layer is what makes the agent actually 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 enough 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.
Make Semantic Kernel actually earn its license fee.
Tell us your two biggest bottlenecks and we'll send back a custom Semantic Kernel implementation blueprint - by email, no call required.
- A specific plan for your Semantic Kernel stack, not a generic pitch
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