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Earn Autonomy Through Visible Evidence
Increase delegation workflow by workflow, using visible evidence, repeated performance, and clear approval boundaries rather than a general feeling of trust.
Parallel Tool Calls Need a Dependency Map
Group calls only when their real inputs are already known, wait at join points, and keep dependent lookups and state changes in sequence.
Remove Prompt Rituals Before Adding More
Maintain prompts like software: retain current requirements, remove obsolete patches, and compare quality, cost, and latency on representative tasks.
Advice or Action? Define the Finish Line
Choose an advisory or execution mode, define the observable end state, and state the boundaries before Claude uses tools.
Scale AI with Layered Enterprise Controls
Scale agentic AI with separate controls for access, connector actions, budgets, and operational visibility instead of relying on one master switch.
Say What You Want, Not Only What to Avoid
Lead with a positive description of the structure, tone, and reading experience you want; use prohibitions only for meaningful boundaries.
Separate Expertise, Files, and Interface
Build maintainable agents by separating reusable procedures, task-specific files, and the interface actions used to complete the work.
Ground Long-Document Answers in Quotes
For large source packs, have Claude select and label the most relevant passages before asking it to interpret or synthesize them.
The Model Proposes; the Harness Enforces
For consequential actions, let Claude prepare a specific proposal while trusted software validates authority, limits, and state before execution.
Replace Prefill with Explicit Structure
Replace seeded assistant openings with an explicit output contract that defines fields, allowed values, order, and preamble behavior.
Turn Feedback into Reviewable Improvements
A controlled improvement loop converts repeated expert feedback into small proposed changes to reusable agent instructions, with review before adoption.
Verify Against Criteria
Turn vague requests to double-check into a short, observable acceptance test that Claude can apply before delivering its answer.
Verify Before You Escalate
A strong review workflow checks possible issues against trusted evidence, resolves routine cases, records support, and escalates only ambiguity or accountable judgment.
Calibrate Thoroughness
Spend additional research and verification effort where uncertainty, contradiction, risk, or expected value can materially change the answer.
Personalization Needs Rules, Not Just Data
Reliable personalization combines a stable template, trusted source data, a defined recipient context, and explicit rules for missing or invalid information.
Parallel When Independent
Run independent tool calls together when all inputs are known. Keep dependent operations sequential, and never invent parameters merely to create concurrency.
Capture Decision Reasoning, Not Just Decisions
Useful organizational memory preserves why a decision made sense, which conditions supported it, and what change should cause the team to reconsider it.
Tell Claude When to Act
Match the verb to the intent. Ask for advice when you want recommendations; explicitly request implementation and define the finished state when you want Claude to act.
Separate Access, Know-How, and Files
Design production agents by separating software access, reusable procedural knowledge, and persistent artifacts. Each capability then becomes easier to govern, reuse, and maintain.
Guide the Reasoning, Do Not Script It
For complex judgment, provide the goal, decision criteria, constraints, and verification target. Prescribe steps when the workflow requires them, not merely to force a reasoning recipe.
Put Humans at the Gates
When AI accelerates execution, automate stable checks and concentrate human judgment at explicit acceptance and exception gates. Approval should trigger the next stage.
Long Context Prompting
For large document sets, put source material first, preserve document identity with clear boundaries, place the task after the evidence, and retrieve relevant support before synthesis.
Execution Boundary and Data Boundary
Where an agent executes and where model inference occurs are separate architecture decisions. Trace execution, storage, inference, and operational ownership individually.
Control the Output Format
Describe the positive shape of the answer Claude should produce. Specify structure, length, style, and exact fields only where they determine whether the result is usable.
Context Has Carrying Cost
Curate active context around the current objective. Preserve durable decisions, compress completed work, isolate noisy exploration, and start fresh when the task changes.
Give Claude a Role
A useful role gives Claude a professional lens: the domain, audience, and priorities that should shape judgment. It focuses behavior but does not replace a clear task.
Measure Work, Not Tokens
AI spending becomes meaningful only when it is connected to completed work and business outcomes. Build a measurement chain from cost, to useful unit, to quality and impact.
Structure Prompts with XML Tags
Use descriptive XML tags to separate instructions, context, examples, and input when a prompt contains several kinds of information. The benefit is clearer boundaries, not decorative complexity.
Use Examples Effectively
Examples are one of the most reliable ways to steer Claude's format, tone, and structure. Instead of only describing what a good answer should look like, demonstrate the pattern you want Claude to reproduce. A useful mental model: Instruction → tells Claude the rule.
Parallelize the Work, Not the Uncertainty
Key concept: Parallelize independent work, then verify before synthesis.
Be Clear and Direct
Be clear and direct. Claude performs better when the prompt makes the desired result obvious. A strong prompt names the goal, adds the minimum context Claude needs, defines constraints, and specifies the output format.
Steering Claude Code
Put each Claude Code instruction in the control surface where it naturally belongs. The practical decision is not “how do I phrase this prompt?” It is: should this be always-on context, a scoped rule, a repeatable skill, an isolated subagent task, or a deterministic hook/permission?
Claude Cowork Product Guide
Key concept: Move from conversational AI to delegated knowledge work.
Building Effective Human-Agent Teams
Key concept: Human-agent teams need explicit team design: public context, defined roles, a north star, verification, and gradual trust-building.
Self-Service Analytics with Claude
Self-service analytics with AI is not mainly a SQL-generation problem. It is a governance, context, metric-definition, retrieval, and verification problem.
Skills as Reusable Workflows
Design AI skills as reusable workflow assets, not as long one-off prompts.
MCP Connectors and Enterprise Authorization
Enterprise AI access should be centrally governed, not individually improvised.
Agent Workflow Patterns
Pick the agent workflow pattern before you build the agent.
Professional-Grade AI
Working at the frontier: How Thomson Reuters builds AI for highstakes professional work.
When to Use Multi-Agent Systems
Key concept: Use multiple agents only when one agent has a clear limitation.
Fix the Loop, Not the Output
Fix the process loop that creates repeated errors instead of manually repairing each individual output.
Zero Trust for AI Agents
Key concept: Give every agent only the identity, permissions, memory, and tools required for its current task.
Secure the AI-Native SDLC
AI-native security is lifecycle design. When agents write, review, test, and coordinate work, controls must be embedded in instructions, identities, permissions, CI, monitoring, and risk-based human review.
GTM Workflow Automation
Start workflow automation from one painful repeated task, then package the solution as a reusable team workflow.
Workflow Orchestration Before Automation
Before automating a workflow, map how information moves across systems, where reconciliation happens, what output is required, and where human approval must remain.
Agent View
Parallel agents increase throughput, but they also create cognitive load. Without a shared control surface, users must remember which sessions are working, blocked, waiting for input, or ready for review. Agent View turns that scattered activity into one visible queue.
Bounded Risk for Agentic AI
Key concept: The goal of AI governance is not zero risk. It is to make agentic risk visible, bounded, and manageable.
Less Context, Better Context
Progressive disclosure: keep a small, stable set of principles in permanent context, then load specialist instructions only when the current task requires them.
Trustworthy Self-Service Analytics
Key concept: Reliable analytics agents need one governed path to the answer—not unrestricted access to every table and historical query.
When to Use Subagents
Key concept: Use subagents when context isolation, parallel execution, or a fresh perspective creates a real advantage.
Put Policy in the Path
Key concept: Move sensitivedata governance from written guidance into an enforced checkpoint on the data path before information reaches the AI model.
Add Context
Add context to improve performance. Claude can follow an instruction more intelligently when it understands the reason or motivation behind it. Context helps the model infer priorities and generalize beyond the exact wording of the instruction.
Risk-Weighted Autonomy
Risk-weighted autonomy: give agents more freedom for routine, reversible, bounded actions and require stronger human review when actions are external, destructive, financial, legally meaningful, production-facing, or difficult to reverse.