Agentic AI Systems: Multi-Agent Orchestration & Tooling

Design, build, evaluate, and operate production-grade agentic AI systems with multi-agent orchestration, tool integration, and enterprise-grade safety controls.

  • Level : Advanced
  • Duration : 24 hours
  • Hands-on Ratio : 70%
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Building Strategic Influence in Matrix Organizations

As GenAI systems grow in complexity, agent-based architectures enable coordinated, modular execution. Agentic AI Systems: Multi-Agent Orchestration & Tooling focuses on designing systems where multiple agents collaborate to complete complex tasks.

The course covers agent roles, orchestration patterns, tool integration, and coordination mechanisms. Participants learn when multi-agent approaches add value, how to manage dependencies, and how to maintain control and observability.

Emphasis is placed on enterprise applicability—ensuring agentic systems remain reliable, secure, and governable. The outcome is architectural readiness to design agent-based solutions that scale beyond single-prompt interactions.

Business Outcomes

Organizations enrolling teams in this course can achieve

  • Reliable Agentic Systems: Agents that plan, execute, and coordinate deterministically under bounded autonomy
  • Safe Tool Integration: Strong controls around tool invocation, approvals, and side-effect management
  • Operational Confidence: Observable, testable, and governable multi-agent workflows suitable for production use

Why You Shouldn’t Miss this course

By the end of this course, participants will be able to:
  • Design agentic architectures using Microsoft Agent Framework and Foundry Agent Service
  • Build tool-using agents with typed contracts, execution safety, and observability
  • Implement multi-agent orchestration patterns with supervision, parallelism, and structured handoffs
  • Manage state and memory using threads, runs, and messages responsibly
  • Evaluate agent reliability and correctness with agent-specific evaluation harnesses

Recommended Experience

Participants should have strong proficiency in Python or .NET, experience integrating APIs, familiarity with LLM concepts, and a working understanding of basic cloud security fundamentals.

Structured for Strategic Application

Bloom-aligned objectives
  • Understand: what differentiates agents from chatbots (planning, tool use, execution, state)
  • Analyze: when agentic architecture is appropriate vs overkill
  • Design: a reference architecture for an enterprise agent runtime
Topics
  • Agentic system components: policy layer, orchestration layer, tool layer, memory layer, evaluation layer, observability layer
  • Agent Framework positioning and core capabilities (Python/.NET, agents + workflows)
  • Foundry Agent Service runtime view: persisted agent definitions and runtime interaction history
  • Architecture patterns:
    • single tool-using agent
    • supervisor + specialist agents
    • workflow/graph-based orchestration (deterministic handoffs)
    • parallel agents with aggregator
Labs
  • Lab 1.1: Target architecture blueprint — Create a reference architecture and deployment topology for a multi-agent system (services, tools, identity, logs).
  • Lab 1.2: Orchestration pattern selection — Map three enterprise tasks to the best orchestration pattern and justify tradeoffs (latency, risk, determinism).
Bloom-aligned objectives
  • Apply: persistent conversation state to multi-turn and long-running tasks
  • Create: a thread/run lifecycle model with retries and idempotency
  • Analyze: failure modes caused by state drift and partial tool completion
Topics
  • Threads, runs, and messages: purpose and lifecycle concepts
  • Handling long-running work:
    • run status polling and streaming patterns
    • run expiration constraints and implications for tool execution timeouts
  • Agent Framework abstractions for multi-turn conversation and threading
  • Idempotency and replay safety:
    • tool call deduplication
    • run resumption design
Labs
  • Lab 2.1: Threaded agent runtime — Implement a minimal runtime that creates threads, appends messages, and starts runs; persist and rehydrate state.
  • Lab 2.2: Run retry + dedupe — Simulate tool timeout and implement safe retries without duplicating side effects.
Bloom-aligned objectives
  • Implement: function calling end-to-end (define → request → execute → return results)
  • Design: typed tool contracts and error handling
  • Evaluate: tool correctness, robustness, and security
Topics
  • Function calling mechanics in Foundry Agent Service:
    • metadata returned by agent (function name + arguments)
    • application executes the function and returns results
  • Tool contract engineering:
    • schemas, validation, typed inputs/outputs
    • error models (transient vs permanent), retry policy, timeouts
  • Tool safety rails:
    • tool allowlists, parameter constraints, PII redaction, audit logging
  • Tool categories:
    • data tools (DB/query, search)
    • action tools (ticket creation, email sending via internal gateway, workflow triggers)
    • compute tools (ETL tasks, batch jobs)
  • Observability for tools:
    • per-tool latency, error rates, failure taxonomy, correlation IDs
Labs
  • Lab 3.1: Build a tool server — Implement a tool gateway (REST) exposing 5 tools: search, create ticket, update ticket, fetch customer profile, write note.
  • Lab 3.2: Function calling integration — Connect the agent to tools using function calling; validate arguments and handle errors safely.
  • Lab 3.3: Tool safety drill — Add allowlists + parameter constraints; simulate an unsafe request and verify the agent refuses or routes to safe alternatives.
Bloom-aligned objectives
  • Understand: different memory types and their risks
  • Apply: memory strategies to improve continuity without leaking sensitive context
  • Create: a memory policy (what to store, where, for how long, and why)
Topics
  • Agent Framework memory options: in-memory history vs persistent stores and variations by agent type
  • Memory design:
    • short-term context window management
    • long-term memory (preferences, user profile abstractions, episodic summaries)
    • task memory (tool outputs, intermediate reasoning artifacts)
  • Safety and governance:
    • retention rules, tenant boundaries, “right to forget” patterns
    • PII handling and redaction before persistence
Labs
  • Lab 4.1: Memory policy implementation — Implement a memory layer that stores conversation summaries and key entities with retention and tenant isolation rules.
  • Lab 4.2: Context budget optimization — Implement a context assembly step that selects only relevant memory and evidence for the next turn.
Bloom-aligned objectives
  • Design: multi-agent roles and coordination protocols
  • Implement: supervisor/specialist and workflow-based orchestration
  • Evaluate: handoff quality and outcome consistency
Topics
  • Role specialization:
    • planner/supervisor agent
    • executor/tool agent
    • reviewer/validator agent
    • domain specialist agents (policy, finance, HR, IT ops)
  • Orchestration styles:
    • supervisor-managed sequential handoffs
    • parallel agents + aggregator
    • deterministic workflows (graph/YAML/visual orchestration in Foundry Agent Service)
  • Conflict resolution:
    • voting/consensus, reviewer arbitration, confidence-based selection
  • Reliability patterns:
    • “plan then act” separation
    • bounded autonomy (max steps, cost ceilings, tool call limits)
Labs
  • Lab 5.1: Supervisor + specialists — Build a supervisor agent that assigns tasks to 3 specialists (Research, Tool Executor, Compliance Reviewer) and merges outputs with citations and audit notes.
  • Lab 5.2: Multi-agent workflow — Implement a workflow where agents run in parallel, then an aggregator produces final output; add stopping criteria and escalation routing.
  • Lab 5.3: Handoff audit trail — Produce a structured handoff log (who decided what, which tool outputs were used, which constraints were applied).
Bloom-aligned objectives
  • Evaluate: agent effectiveness and reliability with agent-specific evaluators
  • Analyze: failure clusters (wrong tool selection, incomplete handoff, state drift, poor intent resolution)
  • Create: promotion gates and regression suites
Topics
  • Foundry agent evaluators and what they measure (agentic workflow evaluation concepts)
  • SDK-based evaluation:
    • converting agent thread data into evaluation data
    • running evaluators over agent runs
  • Custom evaluators (prompt-based / code-based) for:
    • tool correctness
    • policy adherence
    • handoff quality
    • completeness and consistency
  • Regression strategy:
    • golden tasks, adversarial tasks, tool-failure simulations
    • drift detection (tool schema changes, behavior changes)
Labs
  • Lab 6.1: Build an evaluation suite — Create a golden set of 30 tasks with expected tool usage and expected outcomes; add pass/fail criteria.
  • Lab 6.2: Evaluate agent runs via SDK — Convert thread data and run evaluators; generate a promotion report.
Bloom-aligned objectives
  • Apply: safeguards against prompt injection and unsafe tool use
  • Design: governance controls for human-in-the-loop and approvals
  • Evaluate: safety under adversarial prompts and tool misuse attempts
Topics
  • Threat model for agents:
    • tool abuse, escalation of privileges, data exfiltration, instruction hijacking
  • Policy controls:
    • approval steps for high-impact actions
    • environment boundaries (dev/test/prod tools)
  • Auditability:
    • tool call logs, arguments, outputs, decision traces
  • Fail-safe design:
    • safe defaults, rollback hooks, manual override
Labs
  • Lab 7.1: Adversarial tool misuse simulation — Attempt to coerce the agent into unauthorized actions; validate refusal and escalation behavior.
  • Lab 7.2: HITL approvals — Implement “approval required” workflow for destructive actions (e.g., account changes, refunds).
Bloom-aligned objectives
  • Analyze: cost/latency drivers in multi-agent systems
  • Implement: practical optimizations and guardrails
  • Create: an operational runbook for incidents and rollbacks
Topics
  • Performance engineering:
    • concurrency controls, parallelism caps, caching of tool outputs
    • step limits and budget policies
  • Observability:
    • end-to-end tracing across agents and tools
    • run-level metrics (steps, tool calls, retries, failures)
  • Release management:
    • versioning agents/workflows, canary testing, rollback plans
Labs
  • Lab 8.1: Ops dashboard — Build a minimal dashboard and log schema for run tracing, tool metrics, and failure analysis.
  • Lab 8.2: Cost guardrail policy — Implement max-step, max-tool-call, and max-token policies and validate enforcement.
Deliverable Build and demo a multi-agent assistant that:
  • Uses persistent state (threads/runs/messages)
  • Executes a toolchain via function calling (ticketing + knowledge lookup + customer/profile)
  • Uses supervisor + specialists with bounded autonomy
  • Produces an auditable handoff trail
  • Passes an evaluation suite with defined promotion gates
Tools and platforms used
  • Microsoft Agent Framework (Python/.NET agents and multi-agent workflows)
  • Microsoft Foundry Agent Service (agent runtime components, threads/runs/messages, agent definitions)
  • Function calling / tools integration (Foundry agent API)
  • Agent evaluation and custom evaluators (Foundry evaluators + SDK-based evaluation)
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Designed for Immediate Organizational Impact

Includes real-world simulations, stakeholder tools, and influence models tailored for complex organizations.

Orchestration-First Design

Explicit patterns for single-agent, supervised, and multi-agent workflows.

Enterprise Tooling Discipline

Typed tool contracts, allowlists, approval gates, and safe-fail behavior.

High Hands-On Intensity

Agent builds, orchestration drills, safety simulations, and evaluation runs.

Production-Ready Focus

Observability, security, governance, and cost/performance baked in.

This course follows Cognixia’s AI-first, hands-on learning model

Microsoft Foundry + Azure OpenAI access; sandbox APIs (ticketing/CRM); Azure AI Search for retrieval; logging workspace

This course follows Cognixia’s AI-first, reliability-driven engineering model—combining architecture design, hands-on agent builds, failure simulations, and evaluation-based promotion gates to ensure agents behave predictably in enterprise environments.

Let’s Connect

Speak with a Cognixia specialist about enrollment options, custom cohorts for your leadership team, or tailored delivery formats for your organization.

  • Response within 1 business day
  • Available in 5 delivery formats globally
  • Volume pricing for teams of 10+

Get in touch

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Frequently Asked Questions

Find details on duration, delivery formats, customization options, and post-program reinforcement.

No. It focuses on agentic systems that plan, execute tools, and coordinate across workflows.
Yes. Supervision, specialization, parallel execution, and structured handoffs are core topics.
Yes. Agent-specific evaluation and promotion gates are a major emphasis.
Approximately 70% of the course consists of hands-on agent builds, orchestration drills, and reliability testing.
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Why Cognixia for This Course

Cognixia approaches agentic AI as an engineering discipline, not a prompt exercise. This course is designed to help teams build agents that can be trusted to operate across real enterprise workflows involving tools, approvals, and multi-step execution. Participants work hands-on with supervised and multi-agent patterns, tool safety rails, evaluation harnesses, and operational dashboards—mirroring the challenges faced in real production environments. Security, governance, observability, and cost controls are embedded throughout the course, ensuring that agentic systems are deployable, auditable, and sustainable at scale. With deep experience in AI, cloud platforms, and enterprise transformation, Cognixia enables organizations to move confidently from experimental agents to production-ready agentic systems.

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