Semantic Kernel Orchestration

Semantic Kernel — Orchestration Overview

This post introduces orchestration with Microsoft Semantic Kernel. It covers three practical topics:

Definition — What is an Agent?
How to orchestrate agents
The Planning phase (how an agent breaks down and schedules work)

Where helpful, the notes show how these concepts map to a Semantic Kernel implementation (C# / Python snippets are illustrative).

Definition — What is an Agent?

An Agent is a composable, autonomous capability that combines prompts (semantic functions), grounding data (semantic indexes or knowledge bases), and native code (functions or APIs). Agents accept a goal or a task, plan which steps to run, maintain context via memory, and execute multi-step workflows until the goal is satisfied.

In Semantic Kernel terms:

Kernel: the central runtime that wires LLMs, memory providers, and skills together.
Skills & Functions: reusable capabilities. “Semantic functions” are prompt-backed; “native functions” are regular code or API wrappers.
Memory: stores short- and long-term context (vector stores or simple key/value stores).
Planner/Orchestration: composes functions (semantic + native) into a multi-step plan and executes them in order.

Example (conceptual C# snippet):

// Build a kernel and register services (pseudo-code, adapt to your SDK version)
var kernel = new KernelBuilder()
		.WithOpenAITextCompletion("oai", apiKey)
		.WithMemory(new YourVectorStore())
		.Build();

// Register a semantic function (prompt) and a native function (API wrapper)
var summarizer = kernel.CreateSemanticFunction("Summarize", "Summarize the input into 3 bullet points.");
var callApi = kernel.RegisterNativeFunction("CallApi", async (input) => { /* call external API */ });

// An agent coordinates these functions and memory to reach a goal
var agent = new Agent(kernel, skills: new[] { summarizer, callApi });
var result = await agent.RunAsync("Summarize latest customer feedback and call sentiment API if negative");

How to orchestrate agents

Orchestration is the coordination of multiple LLM calls, kernel functions, external APIs, and memory access into a predictable flow. A robust orchestration does the following:

Identify the goal and the capabilities required.
Select or compose kernel functions and prompts (skills).
Control context (pass outputs to next steps, update memory when needed).
Handle errors, retries, and fallbacks.

How this looks in Semantic Kernel:

Compose a skill set: group related functions (e.g., ingestion, summarization, API calls) into a skill.
Use the Planner (or your own orchestration code) to create a sequence of function calls.
Execute the plan using the Kernel invocation API and persist relevant outputs to memory.

Example (pseudo-Python):

# kernel = create_kernel(...)  # wired with LLM + memory
summ = kernel.semantic_function("summarize")
extract = kernel.semantic_function("extract_actions")
notify = kernel.register_native("notify_team")

# Planning (high level):
plan = [summ, extract, notify]
context = {"document": long_text}

for step in plan:
		out = kernel.run(step, context)
		context.update({step.name: out})
		# persist important facts
		kernel.memory.save("last_summary", out.summary)

Notes and best practices:

Keep functions small and well-documented: each should have a clear input/output contract.
Use memory only for necessary context. Persisting everything increases cost and noise.
Use deterministic native code for side-effectful operations (billing, external state changes) and keep LLM calls for reasoning and transformation.
Add failure modes: timeouts, fallbacks to simpler functions, or human-in-the-loop signals.

Planning phase

Planning is the stage when an agent decomposes a high-level goal into ordered sub-tasks, decides which functions to run, and what context each step needs.

Key planning elements:

Capability discovery: which skills/functions are available for this goal?
Input/output schema: what does each step expect and return?
Constraints: cost limits, timeouts, privacy rules, rate limits.
Failure modes and compensation actions: how to rollback or recover.

Planner approaches in Semantic Kernel:

Prompt-based planners: use a semantic function to ask the model to generate a step plan (e.g., a JSON list of actions). This keeps planning flexible but relies on the model’s correctness.
Deterministic planners: code-driven planners that use rules and capability metadata to produce a safe plan.
Hybrid planners: generate a candidate plan with the model, then validate and refine it with deterministic checks or heuristics.

Example (prompt-based planning sketch):

Task: "Onboard a new customer: extract their requirements from the intake form, summarize the priority features, and schedule an internal kickoff."

Prompt to planner function:
"Return a JSON plan with steps. Each step should have: name, function_to_call, inputs, and expected_output_schema."

Model returns:
[
	{"name":"extract_requirements","function":"extract_actions","inputs":["form_text"],"output":{"requirements":"list"}},
	{"name":"summarize","function":"summarize","inputs":["requirements"],"output":{"summary":"string"}},
	{"name":"schedule","function":"create_calendar_event","inputs":["summary","attendees"],"output":{"eventId":"string"}}
]

The agent then validates the plan (check functions exist, inputs match schemas), executes steps, and records outcomes in memory.

Putting it all together — small end-to-end sketch

Register LLM and memory with Kernel.
Register semantic functions (prompts) and native functions (APIs) as skills.
Use a planner to generate an ordered plan for a goal.
Validate the plan and execute step-by-step, persisting relevant context to memory and handling errors.

Practical tips and caveats

Test each function in isolation with unit tests (mock the kernel and memory where possible).
Simulate planner outputs and run through orchestration in a sandbox before enabling side effects.
Monitor costs: each semantic function call can incur LLM cost; batch or summarize to reduce calls.
Secure sensitive data: redact PII before passing to external services or embeddings.