A deep dive into creating intelligent agents that can reason, act, and operate within their computational constraints

Introduction

As AI agents become increasingly sophisticated, developers are discovering that building truly effective autonomous systems requires more than just powerful language models. The key lies in understanding three fundamental principles: how to structure multi-step reasoning and action cycles, how agents perceive and work within their computational boundaries, and why specifications trump creative prompting.

In this post, we'll explore how to build "manus-like" agents—systems capable of complex, multi-round interactions—while understanding their inherent limitations and designing robust specification frameworks.

Understanding the ReAct Paradigm

What is ReAct?

ReAct (Reasoning and Acting) represents a fundamental shift in how we architect AI agents. Unlike simple request-response systems, ReAct agents can:

• Reason about problems step by step
• Act by invoking tools and gathering information
• Reflect on results and adjust their approach
• Iterate through multiple cycles until completion

The Multi-Round Action Architecture

A manus-like agent operates through discrete cycles, each containing three phases:

Phase 1: Observation & Analysis

Agent receives input → Analyzes current state → Identifies required actions

Phase 2: Tool Invocation & Execution

Selects appropriate tools → Executes actions → Gathers results

Phase 3: Synthesis & Planning

Processes results → Updates mental model → Plans next actions

Implementing the ReAct Loop

Here's how to structure a robust ReAct agent:

Step 1: Define the Reasoning Framework

Your agent needs explicit instructions for each phase:

• How to analyze problems systematically
• When to gather more information vs. when to act
• How to evaluate the success of each action
• When to conclude the task is complete

Step 2: Create Action Boundaries

Establish clear rules for:

• Maximum number of iterations per task
• Fallback strategies when tools fail
• Escalation paths for complex problems
• Resource usage limits

Step 3: Build Reflection Mechanisms

After each action, the agent should:

• Evaluate whether the action achieved its intended goal
• Identify any new information that changes the problem space
• Adjust the strategy if the current approach isn't working
• Maintain a running summary of progress

Example ReAct Structure

TASK: Research competitor pricing and create analysis report

ITERATION 1:
Thought: I need to gather pricing data from multiple competitors
Action: web_search("competitor A pricing 2024")
Result: Found pricing page with basic tier info
Reflection: Good start, but need more comprehensive data

ITERATION 2: 
Thought: Need to get detailed pricing for all tiers from competitor A
Action: web_fetch("https://competitor-a.com/pricing")
Result: Complete pricing matrix obtained
Reflection: Have comp A data, need competitors B and C

ITERATION 3:
Thought: Should search for competitor B pricing information  
Action: web_search("competitor B enterprise pricing")
...

The Context Window Constraint: An Agent's Limited Worldview

Understanding Agent Perception

One of the most crucial concepts for agent developers is understanding that agents can only work with information present in their context window. This creates fundamental limitations that shape how we must design agent systems.

What Agents Can and Cannot See

Agents CAN access:

• The initial prompt and instructions
• Previous messages in the current conversation
• Results from tool calls within the current session
• Data retrieved from MCP servers during the current execution
• Their own reasoning and reflection from previous iterations

Agents CANNOT access:

• Information from previous conversations or sessions
• Data not explicitly retrieved through tool calls
• Real-time information unless specifically fetched
• Cached results from previous tool invocations
• "Hidden" or implied context not present in the window

Designing for Context Limitations

Strategy 1: Comprehensive Information Gathering

Since agents forget everything outside their context, ensure they gather all necessary information within each session:

Instead of assuming: "The user mentioned their company's Q3 goals yesterday"
Design for: "Let me search for this user's Q3 goals in their documents"

Strategy 2: Explicit State Management

Build agents that maintain explicit state within their context:

CURRENT SESSION STATE:
- Task: Create marketing analysis
- Data gathered: [list of sources]
- Analysis completed: [list of sections] 
- Remaining work: [list of pending items]

Strategy 3: Tool-First Information Architecture

Design your agent to immediately gather context through tools rather than making assumptions:

WRONG: "Based on our previous discussion about your company..."
RIGHT: "Let me search your documents to understand your company context..."

Context Window Best Practices

Optimize for Retention:

• Summarize key findings after each major tool call
• Maintain running lists of important data points
• Use structured formats that are easy to parse later in the conversation

Plan for Forgetting:

• Design tools that can re-establish context quickly
• Build redundancy into information gathering
• Create clear handoff mechanisms when context limits are reached

Specifications Over Prompts: The Foundation of Reliable Agents

Why Specifications Matter More Than Creative Writing

Many developers approach agent design like creative writing—crafting clever prompts with personality and flair. However, production-ready agents require engineering precision, not creative prose.

The Specification-First Approach

Define Before You Write:

Instead of starting with "You are a helpful AI assistant who...", begin with:

• Input specifications: Exactly what data formats the agent should expect
• Output specifications: Precise formatting requirements for responses
• Behavioral specifications: Clear rules for decision-making
• Error handling specifications: How to respond to edge cases
• Performance specifications: Success criteria and quality metrics

Building Robust Specifications

Input Specification Example:

INPUT FORMAT:
- Task type: [research|analysis|creation|review]
- Scope: [specific parameters and boundaries]
- Required outputs: [list of deliverable formats]
- Constraints: [time, resources, quality requirements]
- Context: [relevant background information]

VALIDATION RULES:
- All required fields must be present
- Task type must match supported capabilities
- Scope must be achievable within context limits

Behavioral Specification Example:

DECISION FRAMEWORK:
1. If task requires external data → use tools to gather information
2. If multiple sources conflict → present options with analysis
3. If insufficient information → explicitly list missing data
4. If task is beyond scope → explain limitations and suggest alternatives
5. If tools fail → implement fallback strategy [defined procedure]

ERROR HANDLING:
- Tool timeout → retry once, then fallback to alternative approach
- Invalid data → clean and validate, or request clarification  
- Partial results → clearly indicate completeness level

Specification Categories

Functional Specifications:

• What the agent must do
• Required capabilities and tools
• Success criteria for each task type

Interface Specifications:

• How users interact with the agent
• Expected input/output formats
• Communication protocols

Quality Specifications:

• Accuracy requirements
• Response time limits
• Error rate tolerances

Operational Specifications:

• Resource usage limits
• Security and privacy requirements
• Integration requirements with other systems

Testing Your Specifications

Specification Validation Process:

1. Edge Case Testing: Create scenarios that push boundary conditions
2. Ambiguity Detection: Identify areas where multiple interpretations are possible
3. Completeness Checking: Ensure all scenarios have defined behaviors
4. Consistency Verification: Check that specifications don't contradict each other

Putting It All Together: A Complete Agent Architecture

The Integrated Approach

A production-ready agent combines all three principles:

ReAct Foundation: Multi-round reasoning and action capabilities Context Awareness: Design that works within computational limits
Specification-Driven: Precise behavioral definitions

Implementation Checklist

ReAct Implementation:

• [ ] Multi-phase reasoning structure defined
• [ ] Tool invocation patterns established
• [ ] Iteration limits and fallbacks configured
• [ ] Reflection and adjustment mechanisms built

Context Management:

• [ ] Information gathering strategies implemented
• [ ] State management within context window
• [ ] Tool-first architecture established
• [ ] Context optimization techniques applied

Specification Framework:

• [ ] Comprehensive input/output specifications
• [ ] Behavioral rules clearly defined
• [ ] Error handling procedures documented
• [ ] Quality and performance criteria established

Advanced Patterns and Considerations

Compound Agent Architectures

For complex tasks, consider breaking work across multiple specialized agents:

• Research Agent: Focused on information gathering
• Analysis Agent: Specialized in data interpretation
• Synthesis Agent: Expert in combining insights into final deliverables

Dynamic Specification Adjustment

Build agents that can adjust their specifications based on:

• Task complexity assessment
• Available time and resources
• User expertise level
• Context richness

Monitoring and Improvement

Implement logging and analytics to track:

• Tool usage patterns and success rates
• Context window utilization efficiency
• Specification adherence and violation patterns
• User satisfaction with agent performance

Conclusion

Building sophisticated AI agents requires moving beyond prompt engineering toward systems engineering. The combination of ReAct reasoning patterns, context-aware design, and specification-driven development creates agents that are both powerful and reliable.

Remember: great agents aren't just well-prompted—they're well-specified, context-aware, and designed to operate effectively within their computational boundaries. Focus on these engineering fundamentals, and you'll build agents that deliver consistent value in production environments.

The future of AI agents lies not in more creative prompts, but in more rigorous engineering. Start with specifications, design for constraints, and implement systematic reasoning—your users will thank you for the reliability.

This post represents current best practices in agent development. As the field evolves rapidly, continue to validate these approaches against your specific use cases and emerging research.