Large Action Models (LAMs) : L'évolution des LLMs vers l'automatisation complète

Large Action Models : LLMs + Exécution actions

Large Action Models (LAMs) sont les successeurs des LLMs. Contrairement aux LLMs purs (génération texte), LAMs comprennent le contexte ET exécutent des actions : appels APIs, interactions UI, orchestration workflows.

Évolution :

2022 : GPT-3 (texte generation)
2023 : GPT-4 (meilleure comprehension)
2024 : Vision+Text multimodal (images)
2025 : Large Action Models (texte + actions) ← NOW

Impact :

Productivity +400% (vs manual)
RPA (Robotic Process Automation) obsolète (too rigid)
Workflow automation autonomous 90%+

Différence LLM vs LAM

LLM (Large Language Model)

Input : "Book meeting with John on Monday 3pm"
Output : "I'll help you book a meeting with John on Monday at 3 PM.
         Please provide John's email and meeting topic."

Action : None (requires human follow-up)

LAM (Large Action Model)

Input : "Book meeting with John on Monday 3pm"

LAM actions :
├─ Search contacts for "John" → finds john@company.com
├─ Check calendar Monday 3pm → available
├─ Create meeting (Outlook API)
├─ Send calendar invite
├─ Set reminder for 30 min before
└─ Reply : "✓ Meeting booked! John received invite."

Action : Complete (autonomous execution)

Architecture LAM

# Pseudo-code LAM architecture

class LargeActionModel:
    def __init__(self):
        self.reasoning_engine = ReasoningLLM()  # Understands intent
        self.tool_executor = ToolExecutor()      # Runs actions
        self.state_manager = StateManager()      # Tracks context

    def execute(self, user_instruction):
        # Step 1 : Understand intent
        intent = self.reasoning_engine.understand(user_instruction)
        # intent = {"action": "book_meeting", "params": {"person": "John", "time": "Monday 3pm"}}

        # Step 2 : Plan steps
        plan = self.reasoning_engine.plan(intent)
        # plan = [search_contacts, check_calendar, create_meeting, send_invite, set_reminder]

        # Step 3 : Execute plan
        for step in plan:
            result = self.tool_executor.run(step)
            if result.error:
                # Handle error, adjust plan
                new_plan = self.reasoning_engine.replan(intent, result.error)

        # Step 4 : Report result
        return f"✓ Meeting booked with {intent['params']['person']}"

Enterprise LAM applications 2025

1. Finance : Invoice automation

Traditional (RPA) :
├─ Extract invoice data (OCR) → 3-5% errors
├─ Input ERP system (slow UI) → 30 sec per invoice
├─ Match PO + validation → manual review 10%
├─ 1000 invoices/day @ 8 hours → 8 FTE needed

LAM-based :
├─ Receipt email trigger → LAM receives attachment
├─ Extract + understand context (90%+ accuracy)
├─ Auto-match PO (intelligent logic)
├─ Create GL entries (ERP APIs)
├─ Flag exceptions (anomalies detected)
├─ 1000 invoices/day @ 2 hours → 1 FTE needed

ROI : 7 FTE × $80k = $560k annual saving

2. HR : Recruitment process

LAM as recruiter assistant :
├─ Application received → Auto-parse resume
├─ Screening questions → Generate + present candidate
├─ Reference check → Contact references (email)
├─ Interview scheduling → Coordinate calendars (3-way)
├─ Offer generation → Draft offer letter
├─ Onboarding prep → Create accounts, send docs

Result : 60% faster hiring (weeks → days)
Quality : Bias reduction (objective evaluation)

3. Customer service

Customer : "My delivery is late"

LAM workflow :
├─ Parse issue
├─ Check shipment status (carrier API)
├─ Analyze delay reason (weather ? logistics ?)
├─ Check customer history (loyal ? first order ?)
├─ Decision tree :
│  ├─ If loyal + first delay → Send $20 credit
│  ├─ If repeat issue → Escalate + 30% refund
│  └─ If weather → Explain + track together
├─ Communicate (send message, resolve)
└─ Record case (CRM auto-update)

Result : 85% of issues resolved autonomously (vs 40% before)

Providers leaders LAM 2025

Automation frameworks :

OpenAI Agents (gpt-4 + actions)
├─ Reasoning : GPT-4 (excellent)
├─ Tool calling : Native APIs
├─ Vision : GPT-4V (images)
└─ Adoption : 100k+ developers

Anthropic's Claude with Tool Use
├─ Safe tool execution (constitutional AI)
├─ Accuracy : Best-in-class
└─ Enterprise adoptions growing

Google Gemini with Actions
├─ Multi-modal (text + image + video)
├─ Google services integration (Workspace, Cloud)
└─ Enterprise features emerging

Specialized platforms :

Zapier AI Actions
├─ 5000+ integrations (tools LAM can use)
├─ No-code workflow builder
└─ SMB focused

Automation Anywhere
├─ Traditional RPA + AI
├─ Enterprise support mature
└─ Legacy customers migrating

UiPath Studio + AI
├─ Visual workflow builder
├─ Process mining (identify automation opportunities)
└─ Enterprise adoption leader

Challenges & limitations LAM 2025

1. Hallucinations in actions

Problem :
Query : "Transfer $50k from account A to account B"

LAM without safety :
├─ Transfer $500k (misread amount)
├─ Transfer to wrong account (hallucinated)
└─ No human verification

Solution :
├─ Confidence thresholds (only execute plus de 95% confidence)
├─ Human review for sensitive actions
├─ Dry-run simulation first
├─ Audit trail mandatory

2. Latency for complex workflows

Workflow : "Process end-of-month close"
└─ Involves : 50+ steps, multiple systems, 2-3 hours

Latency breakdown :
├─ LLM thinking : 30 seconds
├─ API calls (sequential) : 45 minutes
├─ Waiting for approvals : 1+ hour
└─ Retries on failures : Variable

Challenge : Long-running workflows need async execution, state management

3. Cost at scale

Monthly cost scenario (5000 employees) :

LAM inference :
├─ 50k actions/day × $0.003 (avg) = $150/day
├─ 150 × 30 days = $45k/month
└─ At scale : Tools + APIs = +$30k (external integrations)

Total : $75k/month (enterprise budget)
vs human (5 FTE) = $400k/month

But : Requires infrastructure investment first ($200k)
ROI breakeven : 3 months

Governance & safety

Requirements for enterprise LAM

1. Audit trail (every action logged)
2. Approval workflows (sensitive actions require sign-off)
3. Rate limiting (prevent runaway actions)
4. Rollback capability (undo operations)
5. Compliance (audit, data protection)
6. Error handling (graceful degradation)

Example : Finance LAM governance

# Enterprise LAM policy enforcement

class EnterpriseFinanceLAM:
    def __init__(self):
        self.approval_engine = ApprovalEngine()
        self.audit_log = AuditLog()

    def transfer_money(self, from_account, to_account, amount):
        # Step 1 : Validate within policy
        if amount plus de 100000:
            return "DENIED - Requires CFO approval"

        # Step 2 : Execute with audit
        self.audit_log.record({
            "action": "transfer_money",
            "from": from_account,
            "to": to_account,
            "amount": amount,
            "timestamp": now(),
            "executed_by": "LAM"
        })

        # Step 3 : Execute
        result = self.api.transfer(from_account, to_account, amount)

        # Step 4 : Verify
        if not result.success:
            return f"FAILED - {result.error}"

        return "SUCCESS - Transfer completed"

2026 Predictions

LAM trends :

Q1 2026 :
├─ 30% enterprises running 1+ LAM (vs 8% today)
├─ LAM-as-a-Service becomes norm
└─ RPA tools add LAM capabilities (survival mode)

Q2-Q3 2026 :
├─ Horizontal LAMs (multi-industry)
├─ Vertical LAMs (finance, HR, supply chain specific)
└─ Compliance/audit frameworks mature

H2 2026 :
├─ Self-healing systems (LAMs fix their own errors)
├─ Predictive LAMs (prevent issues before they happen)
└─ Mainstream adoption (plus de 50% large enterprises)

Market impact :
├─ RPA market consolidation (fewer players)
├─ BPO (Business Process Outsourcing) disruption
├─ Job market shift (process specialists → prompt engineers)

Articles connexes

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Conclusion : Autonomy meets workflow

LAMs represent the next frontier of GenAI : from intelligent assistance to autonomous execution.

Organizations investing in LAM infrastructure now will capture 3-5x productivity gains over competitors still using manual + RPA.

Key is : Governance + Safety + Clear use cases.

Les entreprises qui réussissent leurs premiers déploiements suivent un schéma commun : cadrer le périmètre (processus, outils autorisés), industrialiser la collecte des feedbacks métiers et établir un comité de gouvernance mixte IT/operations. Les LAMs ne remplacent pas la supervision humaine ; ils déplacent les experts vers des tâches de pilotage et d’optimisation continue. Dans les 12 à 18 prochains mois, les indicateurs clés à surveiller seront le taux d’exécution sans intervention humaine, le temps moyen de remédiation et la capacité à prouver la conformité des actions.

En définitive, les Large Action Models transforment les workflows en actifs stratégiques. Ils relient les systèmes hérités aux plateformes modernes, tout en apportant une couche d’intelligence décisionnelle. Le défi consiste moins à coder un agent qu’à orchestrer un écosystème d’outils, de politiques et de contrôles qui garantissent une automatisation responsable.

Ressources :

OpenAI Function Calling : https://platform.openai.com/docs/guides/function-calling
Anthropic Tool Use : https://docs.anthropic.com/en/docs/build-a-chatbot-with-claude