Scaling business with AI agents

Analyze how next-token prediction directly impacts agent reliability, consistency, and failure modes.

Strategically manipulate context windows, temperature, and token limits to achieve desired agent behaviors.

Identify the key failure modes of LLMs (hallucination, drift, instruction ignoring) and their implications for agent design.

Distinguish between using an LLM as a tool vs. as an autonomous agent.

Map business tasks along an automation spectrum from fully manual to fully agentic, identifying where agents add value and where humans should stay in the loop.

Analyze agentic systems using the perception-reasoning-action-memory framework.

Apply the 'agent-as-employee' mental model to scope what an agent needs to know, do, and escalate.

Select the optimal agent architecture (e.g., reactive, plan-and-execute) for a given business problem by analyzing its requirements.

Explain the principle of least authority as applied to agent tool access and decision boundaries.

Audit a business process to identify the inputs, context, and knowledge an agent needs to perform a task.

Distinguish between static context (system instructions), dynamic context (retrieved data), and conversational context (user input).

Design a context assembly strategy that prioritizes the most relevant information within token limits.

Identify gaps in an agent's information environment that would cause poor or unreliable outputs.

Structure input data formats to maximize agent comprehension and output consistency.

Write system instructions that define an agent's role, tone, boundaries, and decision-making rules.

Use constraint-based prompting to prevent an agent from acting outside its scope.

Design fallback and escalation behaviors for when an agent encounters ambiguity or edge cases.

Apply structured output formatting (e.g., JSON schemas) to enforce consistent agent responses.

Iteratively refine system instructions using failure case analysis.

Write a product requirements document (PRD) for an agentic workflow that defines goals, constraints, inputs, and outputs.

Translate a PRD into an agent specification that defines each agent's role, tools, and boundaries.

Map the information flow between agents in a multi-step workflow, identifying inputs, outputs, and handoff points.

Identify dependencies and potential failure points in a proposed agent workflow from the specification alone.

Use an agent-assisted workflow to generate initial PRDs and specifications from a business problem description.

Explain the retrieval-augmented generation (RAG) pattern and why it solves the knowledge cutoff and hallucination problems.

Analyze the impact of different embedding models and distance metrics on retrieval performance and cost.

Determine what business knowledge to include in a RAG knowledge base and what to exclude.

Select an appropriate chunking method and configure chunk size and overlap for a given document type and retrieval goal.

Identify and address common RAG failure modes: irrelevant retrieval, missing context, and conflicting sources.

Diagnose retrieval quality issues by analyzing chunk relevance and context completeness.

Define tools (APIs, functions, data lookups) an agent can invoke and write clear tool descriptions that guide correct usage.

Design tool selection logic so an agent chooses the right tool for a given sub-task.

Design retry and error-handling patterns for when tool calls fail or return unexpected results.

Apply output validation techniques to verify an agent's work before it reaches the end user.

Diagnose and fix common single-agent failure patterns: looping, tool misuse, and context loss.

Decide when to use a single versatile agent vs. a team of specialized agents for a given workflow.

Design specialized agents with narrow, well-defined roles that compose into a complete workflow.

Design a router agent that delegates incoming tasks to the correct specialized agent based on content.

Design a sequence-managing orchestrator that executes a multi-step workflow across several agents.

Structure inter-agent handoffs so context is preserved and no information is lost between steps.

Trace an end-to-end request through a multi-agent system to identify bottlenecks and failure points.

Define measurable success criteria for an agentic workflow tied to business outcomes.

Design and construct an evaluation dataset with test cases covering normal operation, edge cases, and adversarial inputs.

Execute a benchmark by systematically running an agent against the evaluation dataset and collecting outputs.

Interpret evaluation results to identify specific components (prompts, retrieval, tools) that need improvement.

Incorporate a quality gate into an orchestrator that evaluates agent output against defined standards before proceeding.

Implement and measure the impact of targeted improvements on agent components based on evaluation data.

Identify which decisions in a workflow require human oversight vs. full agent autonomy.

Design approval checkpoints and escalation triggers within an agentic workflow.

Apply prompt-based guardrails to define an agent's safety boundaries and expected behavior.

Design programmatic validation on agent outputs to filter or reject responses that violate safety constraints.

Integrate an external content moderation layer to screen agent inputs and outputs.

Define a graduated autonomy plan: starting with heavy human oversight and reducing it as confidence grows.

Communicate the rationale for human-in-the-loop design to non-technical stakeholders.

Identify the key differences between a working prototype and a production-ready agent system.

Identify critical metrics and data to log for production agent systems, including prompts, responses, tool interactions, and token usage.

Design a monitoring strategy using logged data to detect system failures and performance degradation.

Estimate token usage and API costs for a given agentic workflow based on its design and expected usage patterns.

Apply optimization strategies (model selection, prompt compression, caching) to reduce operational costs of an agentic system.

Apply version control strategies to system instructions and agent configurations to manage their evolution.

Design a lifecycle management process for RAG knowledge bases, including strategies for updates, validation, and deprecation.

Scope a client engagement: translate a business problem into a feasible agentic solution with clear deliverables.

Design a minimal proof-of-concept workflow by selecting the most critical agents, tools, and data needed to demonstrate core value.

Implement the key components of a PoC, including simplified agent instructions and mock tool interfaces.

Test the PoC against representative examples to validate core functionality for a client demonstration.

Present agent system capabilities and limitations to non-technical decision-makers with evidence.

Create technical documentation for an agentic workflow covering architecture, prompt versioning, and knowledge base maintenance.

Develop Standard Operating Procedures (SOPs) for a client to operate, monitor, and handle escalations from the agentic workflow.

Define a pricing and value model for agentic AI services based on outcomes delivered.