Built for Real-World AI Agent Challenges

    From debugging to compliance, see how teams use Prela

    Debug
    Test
    Compare
    Comply
    Use Case 1

    Debug Agent Failures Quickly

    The Problem

    "Agent failed in production. Print statements don't show the reasoning path. How do you debug?"

    The Prela Solution

    • Capture every LLM call, tool invocation, and decision point
    • View full trace tree with timing and context
    • Search traces by error, model, or custom attributes
    • Replay failed trace with different parameters
    Individual developers
    DevOps engineers

    Step-by-Step

    1

    Agent fails with cryptic error

    2

    Run: prela last --status=error

    3

    View trace tree showing exact failure point

    4

    See full context: prompt, tools called, memory state

    5

    Replay with different model/parameters

    6

    Fix identified in 5 minutes (vs 2 hours with print debugging)

    terminal
    $ prela last --status=error

Trace ID: abc123-def456
Status: ERROR
Duration: 2.3s

├─ user_message: "Analyze this document..."
├─ llm_call (gpt-4): 1.2s
│   ├─ tokens: 2,340 in / 890 out
│   └─ cost: $0.12
├─ tool_call (search_docs): 0.8s
│   └─ ERROR: Connection timeout
└─ retry_recommended: true

    75% faster debugging (4 steps → 1 command: prela last)

    Use Case 2

    Test Non-Deterministic Behavior

    The Problem

    "Can't write traditional unit tests for agents (non-deterministic outputs). How do you catch regressions?"

    The Prela Solution

    • Semantic similarity assertions (not exact match)
    • Tool invocation checks (verify correct tools called)
    • Multi-agent assertions (delegation, handoffs, task completion)
    • CI/CD integration with JUnit reporter
    QA engineers
    Platform teams

    Step-by-Step

    1

    Write eval suite with semantic assertions

    2

    Run: prela eval suite.yaml --parallel

    3

    Eval runner executes test cases

    4

    JUnit reporter generates CI/CD-compatible output

    5

    GitHub Actions fails build if assertions fail

    6

    Catch regressions before production

    terminal
    # eval_suite.yaml
cases:
  - name: "Customer support agent responds helpfully"
    input:
      prompt: "How do I reset my password?"
    assertions:
      - type: semantic_similarity
        target: "Check your email for a password reset link"
        threshold: 0.85
      - type: tool_called
        tool_name: "send_password_reset_email"

    Catch regressions before production with CI/CD integration

    Use Case 3

    A/B Test Models Without Re-Running

    The Problem

    "Want to test GPT-4 vs Claude but don't want to re-execute expensive workflows. How do you compare?"

    The Prela Solution

    • Deterministic replay captures full execution context
    • Replay with model override (GPT-4 → Claude-3.5)
    • Semantic comparison with side-by-side diffs
    • Cost estimation before replay
    ML engineers
    Cost-conscious teams

    Step-by-Step

    1

    Run agent with GPT-4 (original execution)

    2

    Capture trace with full context

    3

    Replay with Claude-3.5 (no re-execution of tools/retrieval)

    4

    View semantic diff with similarity score

    5

    Compare cost ($0.60 vs $0.10 = 83% savings)

    6

    Make informed model selection decision

    terminal
    $ prela replay abc123 --model=claude-3-5-sonnet-20241022 \
    --temperature=0.3

Replaying trace abc123...

Original (GPT-4, temp=0.7):
  "Here's a comprehensive analysis..."

Replay (Claude-3.5, temp=0.3):
  "I'll provide a detailed breakdown..."

Semantic Similarity: 88%
Cost: $0.60 → $0.10 (83% savings)

    Compare outputs in seconds, not minutes (instant cost/quality analysis)

    Use Case 4

    Meet Compliance Requirements

    The Problem

    "Compliance asks 'Why did the agent make that decision?' How do you provide an audit trail?"

    The Prela Solution

    • Full audit trail with trace IDs
    • Capture prompts, context, tool calls, memory state
    • Data lineage tracking (document → output mapping)
    • EU AI Act compliance features (Enterprise)
    Compliance teams
    Regulated industries

    Step-by-Step

    1

    Compliance inquiry: 'Why did agent recommend Product X?'

    2

    Look up trace by timestamp or trace_id

    3

    View full execution context and retrieved documents

    4

    Export trace to PDF for compliance documentation

    5

    Show data lineage (which documents influenced output)

    terminal
    $ prela trace show tx-789 --format=detailed

Trace: tx-789
Timestamp: 2024-01-15 14:32:01 UTC
User: [email protected]

Execution Context:
├─ User Prompt: "Recommend a product for..."
├─ Retrieved Documents:
│   ├─ doc_001: Product X specifications
│   └─ doc_002: Customer reviews (4.8★)
├─ LLM Reasoning: "Based on requirements..."
└─ Final Output: "Product X recommended"

Data Lineage: doc_001 → 85% influence

    Answer regulatory inquiries with trace IDs and detailed execution logs

    More Ways Teams Use Prela

    From cost tracking to performance optimization, Prela solves a wide range of AI agent challenges

    Monitor Multi-Agent Systems

    Problem

    Can't visualize agent communication in CrewAI/AutoGen

    Solution

    Agent graph visualization, delegation tracking

    Understand collaboration patterns

    Track LLM Costs

    Problem

    Don't know how much LLM calls cost

    Solution

    Token counting, cost calculation, daily trends

    Budget transparency and cost alerts

    Detect Hallucinations

    Problem

    Agent makes unsupported claims

    Solution

    Hallucination detection with grounding checks

    Catch errors before customers see them

    Optimize Performance

    Problem

    Agent is slow, don't know why

    Solution

    Latency tracing, bottleneck identification

    Find and fix performance issues

    What Teams Are Saying

    Join hundreds of teams building production AI agents with Prela

    "Prela reduced our debugging time by 75%. We can't imagine building agents without it."

    EL

    Engineering Lead

    AI Startup

    "The deterministic replay feature alone is worth the price. We A/B test models daily."

    ME

    ML Engineer

    SaaS Company

    "Compliance was breathing down our neck. Prela gave us the audit trails we needed."

    PT

    Platform Team Lead

    Financial Services

    Ready to Solve Your AI Agent Challenges?

    Start Free – No Credit Card Required

    View Pricing