Building Reliable AI Coding Agents with Skills, MCP and Guardrails Training Course

From autocomplete to agents: understanding agent failure modes

• Core components of a coding agent: model architecture, execution harness, tool interface, context management, and access permissions.

• --> Placement of tools within the ecosystem: Claude Code, GitHub Copilot, Cursor, Codex CLI, and Gemini CLI.

• Failure taxonomy: incorrect context utilization, inappropriate tool selection, insufficient feedback mechanisms, and unbounded autonomy.

Demonstration: Comparative analysis of successful versus unsuccessful task execution.

Context engineering

• Managing the context window as a finite resource: criteria for prioritizing information inclusion.

• Standardized configuration files: AGENTS.md, CLAUDE.md, .cursor/rules, copilot-instructions.md. Unified concept with varying filenames to establish a single source of truth.

• Defining conventions, build and test procedures, and architectural boundaries.

Retrieval augmented generation versus explicit context injection; task decomposition and the use of sub-agents.

Lab: Develop repository context documentation for an unknown Python service, re-execute a previously failing task, and compare outcomes.

Reusable workflows and agent skills

• Selecting the appropriate abstraction level: instruction files, custom skills, custom commands, or standard scripts.

• Skill structure: trigger mechanisms, instructional logic, bundled scripts, and progressive disclosure techniques.

Ensuring portability across platforms while identifying points of vendor lock-in.

• Version control, peer review processes, and team distribution; identification of common anti-patterns.

Lab: Design and validate a reusable workflow that enforces standardized coding conventions.

MCP: integrating agents with external systems

• Architectural components: clients, servers, tools, resources, prompts; standard input/output (stdio) and HTTP transport protocols.

Evaluating server necessity: integration with Git hosting, issue tracking systems, databases, browsers, and internal APIs.

• Determining when traditional command-line interfaces or scripts offer superior reliability compared to MCP servers.

• Maintaining tool-surface hygiene: the impact of excessive tool availability on system reliability for government operations.

Lab: Configure MCP server connections and execute a complete ticket lifecycle—issue creation, branching, patching, testing, and pull request submission.

Feedback loops and evaluation

• Utilizing tests, type checking, and linters as ground truth; employing test-first methodologies as a control mechanism.

Continuous integration (CI) as an outer loop; enforcing rigorous review standards for agent-generated code diffs.

• Developing golden-task evaluation sets: defining key performance indicators and strategies for regression detection.

• Monitoring cost and latency as primary performance metrics.

Lab: Create a small-scale evaluation set and benchmark two agent configurations against established criteria.

Security and guardrails

• Mitigating prompt injection vectors through issues, pull requests, README files, dependencies, and fetched web pages.

• Implementing permission models: allowlists, approval workflows, read-only tool access, and network egress controls for government networks.

Secret management and sandboxing strategies: containerization, ephemeral credentials, and limiting blast radius in the event of a breach.

• Addressing supply-chain risks associated with third-party MCP servers and shared skills.

Lab: Simulate an agent hijacking attempt via a compromised repository, then implement hardening measures to prevent recurrence.

Team-wide deployment

• Implementing a phased adoption strategy; determining elements requiring standardization versus those allowing individual discretion.

• Identifying metrics that demonstrate tangible value and distinguishing them from vanity metrics in the context of government efficiency and accountability.